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COVINA.R
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COVINA.R
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library(xcms)
#library(faahKO)
#library(RColorBrewer)
#library(pander)
#library(magrittr)
library(Kendall)
##Specify the path for the COVINA test dataset
mzXMLPath <- "/Adduct/COVINA Test Dataset"
## Get the full path to the mzXML files
mzXMLs <- dir(mzXMLPath, full.names = TRUE,
recursive = TRUE)
##The raw data files are read in the order of asending CID energy levels
raw_data <- readMSData(files = mzXMLs[c(1,5:8,2:4)],
mode = "onDisk")
mzs <- mz(raw_data)
intensities <- intensity(raw_data)
mzs_by_file <- split(mzs, f = fromFile(raw_data))
intensities_by_file <- split(intensities, f = fromFile(raw_data))
Chrom <- function(sample=1,scans,mzlist,pool.method=sum) {
Intensities.Selected.Scans <- intensities_by_file[[sample]][scans]
mzs.Selected.Scans <- mzs_by_file[[sample]][scans]
#int1 <- intensities_by_file[[1]]
#mzs1 <- mzs_by_file[[1]]
n.scan <- length(scans)
n.mzs <- sapply(Intensities.Selected.Scans, length)
scan.num <- rep(scans, n.mzs)
int <- unlist(Intensities.Selected.Scans)
mzs <- unlist(mzs.Selected.Scans)
if(class(mzlist)=="matrix") {
start <- mzlist[,1]
end <- mzlist[,2]
} else {
start <- mzlist[1]
end <- mzlist[2]
}
cond1 <- outer(mzs, start, ">=")
cond2 <- outer(mzs, end, "<=")
filter <- cond1 & cond2
pool.int <- matrix(0, nrow = n.scan, ncol = ncol(filter))
for(i in 1:ncol(filter)) {
f <- filter[, i]
int.f <- int[f]
scan.f <- scan.num[f]
int.by.scan <- split(int.f, scan.f)
temp <- sapply(int.by.scan, pool.method)
if(length(temp)!=0) {pool.int[(as.integer(names(temp))-scans[1]+1), i] <- temp}
}
return(pool.int)
}
FlipSum <- function (Number.Sequence) {
N <- length(Number.Sequence)
FS <- rep(0,N)
for (i in 1:N) {
FS[i] <- sum(c(rep(-1,(i-1)),rep(1,(N+1-i)))*Number.Sequence)
}
return(FS)
}
InCIDR <- function(Scan.Number=0, Base.mz, Chromatogram.HalfWidth=20, Mass.Tolerance=20,
Number.Of.Samples=1,Intensity.Threshold=0, Correlation.Threshold=0.9,samplenames=NULL) {
#Number.Of.Samples <- 4
Base.Sample <- 1
#Scan.Number <- 199
#Intensity.Threshold <- 50000
Mass.Tolerance <- Mass.Tolerance/10^6
#Base.mz <- 130.087265
#Chromatogram.HalfWidth <- 12
#Correlation.Threshold <- 0.9
Full.Scan.Range <- c(1:length(mzs_by_file[[Base.Sample]]))
Full.Chromatograms <- Chrom(scans=Full.Scan.Range,mzlist=Base.mz*(1+c(-Mass.Tolerance,Mass.Tolerance)))
if(Scan.Number==0) {
Scan.Number<-which.max(Full.Chromatograms)
}
print(paste("Peak is found at Scan",Scan.Number))
SS <- Scan.Number+c((-Chromatogram.HalfWidth*3):(Chromatogram.HalfWidth*3))
PS <- sum(Full.Chromatograms[SS]>(Full.Chromatograms[Scan.Number]/10))
print(paste("Peak width is",PS,"scans at 10% height."))
Scan.Range <- Scan.Number+c(-Chromatogram.HalfWidth:Chromatogram.HalfWidth)
Query.Spectrum <- cbind(mzs_by_file[[Base.Sample]][[Scan.Number]],intensities_by_file[[Base.Sample]][[Scan.Number]])
Query.Spectrum.Trimmed <- Query.Spectrum[intensities_by_file[[Base.Sample]][[Scan.Number]]>Intensity.Threshold,]
mz.Table <- matrix(c(Query.Spectrum.Trimmed[,1]*(1-Mass.Tolerance),Query.Spectrum.Trimmed[,1]*(1+Mass.Tolerance)),ncol=2)
Query.Chromatograms <- Chrom(scans=Scan.Range,mzlist=Base.mz*(1+c(-Mass.Tolerance,Mass.Tolerance)))
Results.Chromatograms <- matrix(0,ncol=Number.Of.Samples*nrow(mz.Table),nrow=length(Scan.Range))
for (i in 1:Number.Of.Samples) {
Results.Chromatograms[,(nrow(mz.Table)*(i-1)+1):(nrow(mz.Table)*i)] <- Chrom(sample=i,scans=Scan.Range,mzlist=mz.Table)
}
Spectra.Cor <- cor(Query.Chromatograms,Results.Chromatograms[,1:nrow(Query.Spectrum.Trimmed)])
if(!sum(Spectra.Cor>Correlation.Threshold)>1)
return(print("No Co-Variant ion found!"))
else (print(paste(sum(Spectra.Cor>Correlation.Threshold)-1,"Co-variant ions found!")))
Spectrum.Covariants <- cbind(Query.Spectrum.Trimmed[Spectra.Cor>Correlation.Threshold,1],
Spectra.Cor[Spectra.Cor>Correlation.Threshold])
InCIDR.Results <- matrix(0,nrow=nrow(Spectrum.Covariants),ncol=(Number.Of.Samples+4))
InCIDR.Results[,1:2] <- Spectrum.Covariants
for (i in 1:Number.Of.Samples) {
CoEluents.Intensities <- Results.Chromatograms
InCIDR.Results[,(2+i)] <- colSums(CoEluents.Intensities[,(match(Spectrum.Covariants[,1],
Query.Spectrum.Trimmed[,1])+(i-1)*nrow(mz.Table))])
}
if(Number.Of.Samples>1) {
InCIDR.Results[,(ncol(InCIDR.Results)-1)] <- apply(InCIDR.Results[,3:(ncol(InCIDR.Results)-2)],MARGIN = 1,
FUN=function(x) Kendall(c(Number.Of.Samples:1),x)$tau)
InCIDR.Results[,(ncol(InCIDR.Results))] <- FlipSum(InCIDR.Results[,(ncol(InCIDR.Results)-1)])
}
if(is.null(samplenames)) {
samplenames <- paste("Sample",c(1:Number.Of.Samples),sep="")
}
colnames(InCIDR.Results) <- c("mz","Correlation",samplenames,"Tau","RankScore")
InCIDR.Results
return(InCIDR.Results)
}
Sample.Names <- c("CID_0eV","CID_2eV",
"CID_4eV","CID_6eV","CID_8eV","CID_10eV",
"CID_15eV","CID_20eV")
Results.Lactate <- InCIDR(Base.mz = 89.024361,Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8,samplenames=Sample.Names) # Lactate
Results.Pyruvate <- InCIDR(Base.mz = 87.008743,Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Pyruvate
Results.Leucine <- InCIDR(Scan.Number = 429,Base.mz = 130.087265,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Leucine
Results.Isoleucine <- InCIDR(Scan.Number = 471,Base.mz = 130.087341,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Isoleucine
Results.Malate <- InCIDR(Base.mz = 133.014221,Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Malate
Results.Glc6P <- InCIDR(Scan.Number = 1180,Base.mz = 259.02240,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Glc6P
Results.Fru6P <- InCIDR(Scan.Number = 1109,Base.mz = 259.02231,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # Fru6P
Results.FBP <- InCIDR(Base.mz = 338.988770,
Chromatogram.HalfWidth = 80,
Intensity.Threshold = 10000,
Number.Of.Samples = 8) # FBP
Results.NAD <- InCIDR(Base.mz = 662.101746,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # NAD
Results.ATP <- InCIDR(Base.mz = 505.988586,
Chromatogram.HalfWidth = 40,
Number.Of.Samples = 8) # ATP