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SpecterQuant.R
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SpecterQuant.R
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SparkSpecterCoeffs = function(rawCoeffsFilepath,ExperimentHeader=NULL, lib=NULL,MS1=FALSE)
{ library(data.table)
DIA_RefSpectraCoeffs = read.csv(rawCoeffsFilepath,header=FALSE,stringsAsFactors = FALSE)
names(DIA_RefSpectraCoeffs) = c("Coeff","Scan","RefPeptideSeq", "RefPrecursorCharge","PrecursorMZ","RetentionTime")
DIA_RefSpectraCoeffs = DIA_RefSpectraCoeffs[c(2,1,3,4,5,6)]
DIA_RefSpectraCoeffs$Coeff = as.numeric(DIA_RefSpectraCoeffs$Coeff)
DIA_RefSpectraCoeffs$Scan = strtoi(DIA_RefSpectraCoeffs$Scan) + 1
DIA_RefSpectraCoeffs$RefPeptideSeq = as.character(DIA_RefSpectraCoeffs$RefPeptideSeq)
DIA_RefSpectraCoeffs$RefPrecursorCharge = strtoi(DIA_RefSpectraCoeffs$RefPrecursorCharge)
DIA_RefSpectraCoeffs$PrecursorMZ = as.numeric(DIA_RefSpectraCoeffs$PrecursorMZ)
DIA_RefSpectraCoeffs$RetentionTime = as.numeric(DIA_RefSpectraCoeffs$RetentionTime)
DIA_RefSpectraCoeffs = na.omit(DIA_RefSpectraCoeffs)
DIA_RefSpectraCoeffs = DIA_RefSpectraCoeffs[with(DIA_RefSpectraCoeffs,order(Scan, RefPeptideSeq, RefPrecursorCharge)),]
#Convert minutes to seconds if need be
if (max(DIA_RefSpectraCoeffs$RetentionTime) < 600)
DIA_RefSpectraCoeffs$RetentionTime = 60*DIA_RefSpectraCoeffs$RetentionTime
DIA_RefSpectraCoeffs = DIA_RefSpectraCoeffs[which(DIA_RefSpectraCoeffs$Coeff != 0),]
DIA_RefSpectraCoeffs = data.table(DIA_RefSpectraCoeffs)
setkey(DIA_RefSpectraCoeffs,RefPeptideSeq,RefPrecursorCharge)
return(DIA_RefSpectraCoeffs)
}
QuantifyAllFromSpecterCoeffs = function(SpecterData,header) {
require(kza)
require(pracma)
require(zoo)
require(moments)
require(data.table)
IDs = data.frame(unique(SpecterData[,c('RefPeptideSeq','RefPrecursorCharge'),with=FALSE]))
IsThereEnoughData = unlist(Map(function(i) EnoughData(SpecterData,IDs,i,RTWindow=FALSE),
seq(1,nrow(IDs))))
f = function(k) {if (IsThereEnoughData[k]) {
SpecterQuantifyPeptides(SpecterData,IDs,k,header,RTWindow=FALSE)} else {list(0,1,0,0,0,0,1,0,0,0)}}
Quant = Map(f,seq(1,nrow(IDs)))
Quants = IDs
Quants$SpecterAreaQuant = as.numeric(unlist(lapply(Quant,function(l) l[[1]][1])))
Quants$SpecterLBPval = as.numeric(unlist(lapply(Quant,function(l) l[[2]][1])))
Quants$SpecterSNR = as.numeric(unlist(lapply(Quant,function(l) l[[3]][1])))
Quants$MaxSpecterCoeffTime = as.numeric(unlist(lapply(Quant,function(l) l[[4]][1])))
Quants$MaxSpecterCoeff = as.numeric(unlist(lapply(Quant,function(l) l[[5]][1])))
Quants$SpecterPeakWidth = as.numeric(unlist(lapply(Quant,function(l) l[[6]][1])))
Quants$SpecterLBPvalOnUniformGrid = as.numeric(unlist(lapply(Quant,function(l) l[[7]][1])))
Quants$PeakVariance = as.numeric(unlist(lapply(Quant,function(l) l[[8]][1])))
Quants$PeakSkewness = as.numeric(unlist(lapply(Quant,function(l) l[[9]][1])))
Quants$PeakKurtosis = as.numeric(unlist(lapply(Quant,function(l) l[[10]][1])))
return(Quants)
}
###################### PROFILE SMOOTHING AND PLOTTING ###########################
SmoothProfile = function(Data,Identifiers,i,header,IntensityCutoff=0,QuantileCutoff=0,RTWindow=TRUE,param=1e-2,type="rollmean",
Plot=TRUE,FilterWindow= 3,KZiters=3,verbose=FALSE,MS1=FALSE,Experiment=NULL,Header=NULL) {
PeptideData = data.frame(Data[list(Identifiers$RefPeptideSeq[i],Identifiers$RefPrecursorCharge[i])])
PrecMZ = unique(PeptideData$RefPrecursorMZ)
PeptideDataNoCutoff = data.frame(PeptideData)
if (RTWindow)
{PeptideData = PeptideData[which(abs(PeptideData$RetentionTime - PeptideData$RefRetentionTime) < 300),]
PeptideDataNoCutoff = PeptideDataNoCutoff[which(abs(PeptideDataNoCutoff$RetentionTime - PeptideDataNoCutoff$RefRetentionTime) < 300),]}
if (verbose)
print(PeptideData)
PeptideData = PeptideData[which(PeptideData$Coeff >= IntensityCutoff),]
PeptideData = PeptideData[which(PeptideData$Coeff >= quantile(PeptideData$Coeff,QuantileCutoff)),]
PeptideDataNoCutoff = PeptideDataNoCutoff[c("RetentionTime","Coeff","PrecursorMZ")]
PeptideDataNoCutoff = PeptideDataNoCutoff[!duplicated(PeptideDataNoCutoff$RetentionTime),]
PeptideData = PeptideData[c("Scan","RetentionTime","Coeff","PrecursorMZ")]
PeptideData = PeptideData[!duplicated(PeptideData$RetentionTime),]
SmoothFrame = data.frame(RetentionTime=numeric(),Coeff=numeric())
KZData = NULL
if (type == "SG")
{SmoothedData = sgolayfilt(PeptideDataOnGrid[,2],p=0,n=3)
SmoothFrame = data.frame(RetentionTime=PeptideDataOnGrid[,1],Coeff=SmoothedData)
}
if (type == "rollmean")
{ZooData = zoo(PeptideData$Coeff, PeptideData$RetentionTime)
SmoothedData = zoo(0,0)
PeptideData$Coeff[which(PeptideData$Coeff < 1)] = 0
if (nrow(PeptideData) > 3) {
MinScanIndex = min(PeptideData$Scan,na.rm=TRUE)
MaxScanIndex = max(PeptideData$Scan,na.rm=TRUE)
HeaderRange = data.frame(header[J(MinScanIndex:MaxScanIndex)]) #header needs to be a data.table with key seqNum
HeaderRange = subset(HeaderRange,msLevel == 2 & floor(precursorMZ) %in% floor(unique(PeptideData$PrecursorMZ)))
DataOnUniformGrid = HeaderRange[c("seqNum","retentionTime")]
DataOnUniformGrid$Coeff = numeric(nrow(DataOnUniformGrid))
DataOnUniformGrid$Coeff[which(DataOnUniformGrid$seqNum %in% PeptideData$Scan)] = PeptideData$Coeff
DataOnUniformGrid = DataOnUniformGrid$Coeff
KZData = kz(DataOnUniformGrid,m=FilterWindow,k=KZiters)}
}
BoxTestPval = 1
if (Plot) {
n = nrow(PeptideDataNoCutoff)
m = nrow(PeptideData)
CombinedData = rbind(PeptideDataNoCutoff,PeptideData)
CombinedData = rbind(CombinedData,KZFrame)
CombinedData$Type = paste0("Kolmogorov-Zurbenko"," (",KZiters," iterations)")
CombinedData$Type[1:n] = "Raw"
CombinedData$Type[(n+1):(n+m)] = "Cutoff"
CombinedData$Type = factor(CombinedData$Type, levels=c("Raw","Cutoff",paste0("Kolmogorov-Zurbenko"," (",KZiters," iterations)")))
if (MS1)
{
MS1Data = Header[which(Header$msLevel == 1),]
PrecursorMS1Candidates = findInterval(PeptideDataNoCutoff$RetentionTime,MS1Data$retentionTime)
PrecursorMS1CandidateScans = mzR::peaks(Experiment,scans=which(Header$msLevel == 1)[PrecursorMS1Candidates])
MS1Intensity = data.frame(RetentionTime=numeric(),Coeff=numeric())
for (k in 1:length(PrecursorMS1Candidates))
{if (any(abs(PrecursorMS1CandidateScans[[k]][,1] - PrecMZ) < 1e-5*PrecMZ))
{ClosestPeak = which(abs(PrecursorMS1CandidateScans[[k]][,1] - PrecMZ) == min(abs(PrecursorMS1CandidateScans[[k]][,1] - PrecMZ)))
ClosestPeakIntensity = PrecursorMS1CandidateScans[[k]][ClosestPeak,2]
MS1Intensity = rbind(MS1Intensity,data.frame(RetentionTime=MS1Data$retentionTime[PrecursorMS1Candidates[k]],Coeff=ClosestPeakIntensity,
PrecursorMZ = PrecursorMS1CandidateScans[[k]][ClosestPeak,1]))
}
}
MS1Intensity$Type = "MS1 (closest precursor m/z)"
CombinedData = rbind(CombinedData,MS1Intensity)
CombinedData$Type = factor(CombinedData$Type, levels=c("Raw","Cutoff","Moving average",paste0("Kolmogorov-Zurbenko"," (",KZiters," iterations)"),
"MS1 (closest precursor m/z)"))
}
p = ggplot(CombinedData,aes(x=RetentionTime,y=Coeff,group=1)) +
geom_line(size=1) + facet_wrap(~Type,ncol=1) + theme(legend.title=element_blank()) +
ggtitle(paste0(Identifiers$RefPeptideSeq[i],", Charge = ",Identifiers$RefPrecursorCharge[i],
", RefPrecursorMZ = ",round(PrecMZ,digits=4),
", Cutoff = ",quantile(PeptideData$Coeff,QuantileCutoff),
", Box test p-val = ",BoxTestPval))
print(p)
}
return(KZData)
}
###################### PEAK DETECTION ###########################
FindPeaksInData = function(Data,Identifiers,i,header,Multiplexed=FALSE,IntensityCutoff=0,
QuantileCutoff=0,RTWindow=TRUE,smooth="rollmean",FilterWindow= 3,KZiters=3) {
PeptideData = data.frame(Data[list(Identifiers$RefPeptideSeq[i],Identifiers$RefPrecursorCharge[i])])
if (RTWindow)
PeptideData = subset(PeptideData, abs(RetentionTime - RefRetentionTime) < 300)
PeptideData = PeptideData[c("Scan","RetentionTime","Coeff","PrecursorMZ")]
PeptideData = PeptideData[!duplicated(PeptideData$RetentionTime),]
PeptideData$Coeff[which(PeptideData$Coeff < 1)] = 0
PeptidePeaks = NULL
if (length(which(PeptideData$Coeff > 0) > 3)) {
MinScanIndex = min(PeptideData$Scan,na.rm=TRUE)
MaxScanIndex = max(PeptideData$Scan,na.rm=TRUE)
HeaderRange = data.frame(header[J(MinScanIndex:MaxScanIndex)]) #header needs to be a data.table with key seqNum
HeaderRange = subset(HeaderRange,msLevel == 2 & floor(precursorMZ) %in% floor(unique(PeptideData$PrecursorMZ)))
DataOnUniformGrid = HeaderRange[c("seqNum","retentionTime")]
DataOnUniformGrid$Coeff = numeric(nrow(DataOnUniformGrid))
DataOnUniformGrid$Coeff[which(DataOnUniformGrid$seqNum %in% PeptideData$Scan)] = PeptideData$Coeff
DataOnUniformGrid = DataOnUniformGrid$Coeff
PeptidePeaks = findpeaks(DataOnUniformGrid, nups = 2, ndowns = 2, npeaks= 10,sortstr = TRUE)
if (smooth == "rollmean")
{
KZData = kz(DataOnUniformGrid,m=FilterWindow,k=KZiters)
KZData[which(DataOnUniformGrid < 1)] = 0 #Remove artifical nonzero coeffs resulting from smoothing
PeptidePeaks = findpeaks(KZData, nups = 2, ndowns = 2, npeaks= 10,sortstr = TRUE)
}
}
return(PeptidePeaks)
}
###################### QUANTITATION ###########################
SpecterQuantifyPeptides = function(Data,Identifiers,i,header,IntensityCutoff=0,QuantileCutoff=0,RTWindow=TRUE,smooth="rollmean",FilterWindow= 3,KZiters=3) {
PeptideData = data.frame(Data[list(Identifiers$RefPeptideSeq[i],Identifiers$RefPrecursorCharge[i])])
if (RTWindow)
PeptideData = subset(PeptideData, abs(RetentionTime - RefRetentionTime) < 300)
PeptideData = PeptideData[c("Scan","RetentionTime","Coeff","PrecursorMZ")]
PeptideData = PeptideData[!duplicated(PeptideData$RetentionTime),]
PeptideData$Coeff[which(PeptideData$Coeff < 1)] = 0 #Total ion intensities less than 1 are discarded as physically meaningless.
area = 0
RawPeaks = FindPeaksInData(Data,Identifiers,i,header,IntensityCutoff=IntensityCutoff,
QuantileCutoff=QuantileCutoff,RTWindow=RTWindow,smooth="none",FilterWindow=FilterWindow)
SmoothPeaks = FindPeaksInData(Data,Identifiers,i,header,
IntensityCutoff=IntensityCutoff,QuantileCutoff=QuantileCutoff,RTWindow=RTWindow,smooth=smooth,FilterWindow=FilterWindow,KZiters=KZiters)
BoxTestPval = 1
BoxTestPvalOnGrid = 1
MaxCoeff= 0
TimeAtTopOfPeak = 0
snr = 0
PeakCentralMoments = numeric(3)
result = list(Area=area,BoxTestPval=BoxTestPval,SNR=snr,RT=TimeAtTopOfPeak,MaxCoeff=0,PeakWidth=0,BoxTestPvalOnGrid=BoxTestPvalOnGrid,
Variance=0,Skewness=0,Kurtosis=0)
if (length(which(PeptideData$Coeff > 1) > 3) & !is.null(SmoothPeaks)) {
MinScanIndex = min(PeptideData$Scan,na.rm=TRUE)
MaxScanIndex = max(PeptideData$Scan,na.rm=TRUE)
HeaderRange = data.frame(header[J(MinScanIndex:MaxScanIndex)]) #header needs to be a data.table with key seqNum
HeaderRange = subset(HeaderRange,msLevel == 2 & floor(precursorMZ) %in% floor(unique(PeptideData$PrecursorMZ)))
DataOnUniformGrid = HeaderRange[c("seqNum","retentionTime")]
DataOnUniformGrid$Coeff = numeric(nrow(DataOnUniformGrid))
DataOnUniformGrid$Coeff[which(DataOnUniformGrid$seqNum %in% PeptideData$Scan)] = PeptideData$Coeff
start = SmoothPeaks[1,3]
end = SmoothPeaks[1,4]
PeakDataOnUniformGrid = DataOnUniformGrid[start:end,]
area = trapz(PeakDataOnUniformGrid$retentionTime, PeakDataOnUniformGrid$Coeff)
TimeAtTopOfPeak = DataOnUniformGrid$retentionTime[SmoothPeaks[1,2]]
MaxCoeff=DataOnUniformGrid$Coeff[SmoothPeaks[1,2]]
start = SmoothPeaks[1,3]
end = SmoothPeaks[1,4]
PeakDataOnUniformGrid = DataOnUniformGrid[start:end,]
PeakCentralMoments = all.moments(scale(PeakDataOnUniformGrid$Coeff,center=FALSE),order.max = 4,central = TRUE)[3:5]
BoxTest = Box.test(PeptideData$Coeff,type="Ljung-Box")
BoxTestPval = round(BoxTest$p.value,digits=5)
BoxTestOnGrid = Box.test(DataOnUniformGrid$Coeff,type="Ljung-Box")
BoxTestPvalOnGrid = round(BoxTestOnGrid$p.value,digits=5)
snr=area/sd(DataOnUniformGrid$Coeff[-seq(start,end)]) #Denominator is the standard deviation of the coefficients not falling within the highest peak
peakWidth = DataOnUniformGrid$retentionTime[end] - DataOnUniformGrid$retentionTime[start]
result = list(Area=area,BoxTestPval=BoxTestPval,SNR=snr,RT=TimeAtTopOfPeak,MaxCoeff=MaxCoeff,PeakWidth=peakWidth,
BoxTestPvalOnGrid=BoxTestPvalOnGrid,Variance=PeakCentralMoments[1],Skewness=PeakCentralMoments[2],Kurtosis=PeakCentralMoments[3])
}
return(result)
}
EnoughData = function(Data,Identifiers,i,RTWindow=TRUE) {
PeptideData = Data[list(Identifiers$RefPeptideSeq[i],Identifiers$RefPrecursorCharge[i])]
if (RTWindow)
PeptideData = subset(PeptideData, abs(RetentionTime - RefRetentionTime) < 300)
if (nrow(PeptideData) > 0)
{return(TRUE)} else {
return(FALSE)
}
}
library(MASS)
library(data.table)
args=commandArgs(TRUE)
mzmlPath = paste0(args[1],".mzML")
dirName = strsplit(mzmlPath,split="/")[[1]]
mzmlName= gsub(".mzML","",dirName[length(dirName)])
libName = strsplit(args[2],split="/")[[1]]
libName = libName[length(libName)]
dirName = dirName[-length(dirName)]
dirName = paste(dirName,collapse="/")
resultsPath = paste0(dirName,"/SpecterResults/",mzmlName,"_",libName,"_SpecterCoeffs.csv")
headerPath = paste0(dirName,"/SpecterResults/",mzmlName,"_",libName,"_header.csv")
decoyResultsPath = paste0(gsub(".csv","",resultsPath),"Decoys.csv")
h = read.csv(headerPath,header=FALSE,stringsAsFactors=FALSE)
names(h) = c("precursorMZ","retentionTime","seqNum")
h$retentionTime = 60*h$retentionTime
h$seqNum = h$seqNum + 1
h$msLevel = 2
h = data.table(h)
setkey(h,seqNum)
results = SparkSpecterCoeffs(resultsPath)
resultsWithDecoys = SparkSpecterCoeffs(decoyResultsPath)
quants = QuantifyAllFromSpecterCoeffs(results,header = h)
quantsWithDecoys = QuantifyAllFromSpecterCoeffs(resultsWithDecoys,header=h)
quantsWithDecoys$Type = ifelse(grepl("DECOY",quantsWithDecoys$RefPeptideSeq),"Decoy","Target")
quantsWithDecoys = quantsWithDecoys[which(quantsWithDecoys$SpecterAreaQuant > 0),]
SpecterLDA = lda(Type~ MaxSpecterCoeff + PeakVariance + PeakSkewness + PeakKurtosis,
data = quantsWithDecoys)
plda = predict(object = SpecterLDA, newdata = quantsWithDecoys)
D = data.frame(PeptideSeq = quantsWithDecoys$RefPeptideSeq,
PrecursorCharge = quantsWithDecoys$RefPrecursorCharge,
Type=quantsWithDecoys$Type,
Score=as.numeric(unlist(plda$x)))
FDR = function(score) {
return(length(which(D$Type == "Decoy" & D$Score > score))/length(which(D$Score > score)))
}
fdr = as.numeric(sapply(D$Score,FDR))
cutoff = min(D$Score[which(fdr < 0.01)],na.rm=TRUE)
D = D[which(D$Score >= cutoff),]
quants = quants[which(paste(quants$RefPeptideSeq,quants$RefPrecursorCharge) %in% paste(D$PeptideSeq,D$PrecursorCharge)),]
quants$Score = D$Score[match(paste(quants$RefPeptideSeq,quants$RefPrecursorCharge),paste(D$PeptideSeq,D$PrecursorCharge))]
outputPath = gsub("_SpecterCoeffs.csv","_SpecterQuants.csv",resultsPath)
write.csv(quants[c("RefPeptideSeq","RefPrecursorCharge","SpecterAreaQuant","Score")],file=outputPath,quote=FALSE,row.names=FALSE)