Nothing
PeakML.Plot.RelatedPeaks <- function (filename, ionisation="detect", Rawpath=NULL, DBS=dir(paste(find.package("mzmatch.R"), "/dbs", sep=""),full.names=TRUE),outputfile,sampleClasses=NULL)
{
plotPeak <- function (peakn)
{
peakmass <- round(Masses[which.peaksets[peakn]]-(PeakMLdata$massCorrection[[1]]),7)
peakRT <- round(RTs[which.peaksets[peakn]],0)
peakInt <- format(round(Intensities[which.peaksets[peakn]],0),scientific=TRUE)
adduct <- PeakMLdata$GroupAnnotations$relation.ship[which.peaksets[peakn]]
infmat <- matrix(c(peakmass,peakRT,peakInt,adduct),ncol=1,nrow=4)
rownames(infmat) <- c("m/z","RT, s","Intensity","Derivative")
textplot(infmat,halign="left",valign="top",mar=c(0,0,0,0),show.colnames=FALSE)
idents <- id.resolved[[which.peaksets[peakn]]]
if (!all(is.na(idents)))
{
idents[,6] <- round(as.numeric(idents[,6]),1)
colnames (idents) <- c("id","Formula","Mass","Name","DB","ppm","Addcut")
textplot(idents[,c(2,7,6,4,5)],halign="left",valign="top",mar=c(0,0,0,0.5),show.rownames=FALSE)
} else
{
plot(1,1,xlab="",ylab="",pch="",axes=FALSE)
}
groupid <- which.peaksets[peakn]
PeakML.Plot.Chromatograms(PeakMLdata=PeakMLdata,groupid=groupid,sampleClasses=sampleClasses,xaxis=FALSE)
}
st <- system.time (PeakMLdata <- PeakML.Read (filename,ionisation,Rawpath))
PeakTable <- PeakML.Methods.getCompleteTable (PeakMLdata)
id.resolved <- PeakML.Methods.DBidToCompoundName(DBS=DBS, PeakMLdata=PeakMLdata, collapse=FALSE)
Masses <- apply(PeakTable[[2]],2,median,na.rm=TRUE)
RTs <- apply(PeakTable[[3]],2,median,na.rm=TRUE)
Intensities <- apply(PeakTable[[1]],2,max,na.rm=TRUE)
## How many cluster there are
peak.clusters <- unique(PeakMLdata$GroupAnnotations$relation.id)
pdf (file=paste(outputfile,".pdf",sep=""),paper="A4",width=8,height=11.4)
for (pclust in 1:length(peak.clusters))
{
which.peaksets <- which(PeakMLdata$GroupAnnotations$relation.id==peak.clusters[pclust])
col.vector <- rep(NA,length(which.peaksets))
col.vector[is.na(id.resolved[which.peaksets])] <- 1
col.vector[is.na(col.vector)] <- 2
layout (matrix(c(1,1,1,2:16),ncol=3,nrow=6,byrow=TRUE),widths=c(0.25,0.55,0.2))
par (mar=c(4,4,1,0))
ymax <- max(Intensities[which.peaksets])+(max(Intensities[which.peaksets])/10)
plot (Masses[which.peaksets]-(PeakMLdata$massCorrection[[1]]),Intensities[which.peaksets],type="h",xlab="m/z",ylab="Intensity",lwd=1.5,col=col.vector,ylim=c(0,ymax),font.lab=2)
legend ("topright", fill=c(2),c("putatively annotated"))
text (Masses[which.peaksets]-(PeakMLdata$massCorrection[[1]]),Intensities[which.peaksets],round(Masses[which.peaksets]-(PeakMLdata$massCorrection[[1]]),5),pos=3,cex=0.9,font=2,col=col.vector)
RTtime <- round(median(RTs[which.peaksets]),0)
RTtimemin <- floor(RTtime/60)
RTtimemin <- c(RTtimemin,RTtime-(RTtimemin*60))
mtext (paste("RT: ",RTtimemin[1],"min ",RTtimemin[2],"s (",RTtime,"s)",sep=""),side=3,cex=0.9)
if (length(which.peaksets)<=5)
{
maxnum <- length(which.peaksets)
} else
{
maxnum <- 5
}
for (peakn in 1:maxnum)
{
plotPeak(peakn)
}
if (length(which.peaksets)>5)
{
# how many extra pages to create
peak.dif <- length(which.peaksets)-5
number.of.pages <- ceiling(peak.dif/6)
for (pgn in 1:number.of.pages)
{
maxnum <- c((pgn*6):((pgn*6)+5))
if (max(maxnum)>length(which.peaksets))
{
maxnum <- c(maxnum[1],length(which.peaksets))
maxnum <- unique(maxnum)
}
layout (matrix(c(1:18),ncol=3,nrow=6,byrow=TRUE),widths=c(0.25,0.55,0.2))
for (peakn in maxnum)
{
plotPeak(peakn)
}
}
}
}
dev.off ()
}
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