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Auto.MV.Regress.R
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Auto.MV.Regress.R
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Auto.MV.Regress<-function( X="PCA.outliers.removed.csv", Yvar="Y.outliers.removed.csv", non.zero=2, Corr.thresh=0.3, pvalue=0.05, p.adjust.methods="none", heatmap=TRUE, hclust.method="complete",dist.m.method="euclidean",Clust.ppm=10, Clust.RT.tol=2, mode="negative", box.prop=0.2, Yunits="Y_units", HMDBtol=0.005,wd="~/STUDY NAME/"){
###load package dependencies
require(gplots)
require(ggplot2)
require(sfsmisc)
require(RColorBrewer)
setwd(wd)
###change arguments to lower case so not mistaken for function calls
if(hclust.method=="WARD.D")
{
hclust.method<-"ward.D"
} else if (hclust.method=="WARD.D2")
{
hclust.method<-"ward.D2"
} else {
hclust.method<-tolower(hclust.method)
}
dist.m.method<-tolower(dist.m.method)
p.adjust.methods<-tolower(p.adjust.methods)
message("Reading Auto.PCA output file...PLEASE WAIT")#,quote=F)
flush.console()
Samples<-read.csv(X,header=T)
message("...Done")#,quote=F)
flush.console()
###identify and store as objects EIC/ident, m/z and RT column names
EIC.column.name<-colnames(Samples)[1]
mzmed.column.name<-colnames(Samples)[2]
RTmed.column.name<-colnames(Samples)[3]
Y<-as.data.frame(read.csv(Yvar,header=T,row.names=1))
Ycolnames<-as.character(colnames(Y))
Yrownames<-as.character(row.names(Y))
####create Auto.MV.Regress subdirectory to keep everything tidy!####
wd<-paste(substr(wd,1,nchar(wd)-17),"/Auto.MV.Regress.results","/",sep="")
dir.create(wd)
setwd(wd)
###save all parameters used in a dated .csv file for future reference###
Parameters<-data.frame(X,Yvar,p.adjust.methods,box.plot.proportions=box.prop,
hclust.method,Pearson.corr.thresh=Corr.thresh,pvalue.cutoff=pvalue,
Cluster.RT.tol=Clust.RT.tol,Mass.Accuracy.ClusterID=Clust.ppm,Min.pos.values.Y=round(((nrow(Y)/100)*non.zero),digits=0))
date<-Sys.time()
date<-gsub("-",".",date)
write.csv(Parameters,paste("Parameters",substr(date,1,10),".csv",sep=" "),row.names=FALSE)
XCMScolumnsIndex<-c(1:which(colnames(Samples)=="RSD_corr_below"))
XCMScolumns<-Samples[,XCMScolumnsIndex]
Samples<-Samples[,-XCMScolumnsIndex]
###check if Y variable names are the same as sample column names if not stop the process###
if(length(which(colnames(Samples) %in% row.names(Y)==T)) != ncol(Samples))
{
tkmessageBox(message ="The Y variable sample names in the 1st column do not match the
PCA_outliers_removed.csv sample names!
Please make sure the names match in the .csv files, change if necessary and start again.",title="ERROR")
stop()
}
#SampleIndex<-colnames(t(Y)) %in% colnames(Samples)
#Y<-data.frame(Y[SampleIndex,])
Yindices<-as.data.frame(Y>0) # all non zero Y-variables for regression
Yzeroindex<-as.data.frame(Y==0)
Yindices_sum<-as.data.frame(apply(Yindices,2,sum)) # sum of all non-zero Y-variables
Yabove_10<-which(Yindices_sum>=((nrow(Y)/100)*non.zero)) # index all Y-variables with less than minimum proportion of non-zeros
Yindices<-as.data.frame(Yindices[,Yabove_10]) # remove all Y-variables from Yindices
Y<-as.data.frame(Y[,Yabove_10])
colnames(Y)<-Ycolnames[Yabove_10]
row.names(Y)<-Yrownames
##########################################################################################
results <- data.frame() # empty data.frame for storage of results above threshold
above_threshold_df<-data.frame()
box.plot.scores<-as.numeric()
Biomarker_numbers.df<-data.frame()
if (ncol(Y)>0){
for (k in 1:ncol(Y)){
foldername<-colnames(Y[k]) # new folder name
dirname<-paste(wd,foldername,sep="") #directory name
dir.create(dirname) # create new folder for individual Y-variable data processing
setwd(dirname) # set working directory to new folder
Ydata<-Y[,k] # Y variable
Yindex<-Yindices[,k] # Non zero index
Xsamplesincluded<-Samples[,Yindex] # X samples to include in regression
Yincluded<-Ydata[Yindex] # Y variables to include in regression
Pcor<-cor(t(Xsamplesincluded),Yincluded,method=c("pearson")) #X-Y correlation
if (any(is.na(Pcor)==TRUE)==FALSE){
# Pcor probability function f test and t stat
dfr<-ncol(Xsamplesincluded)-2 #degrees of freedom
r2<-Pcor^2 #coefficient of determination
Fstat<-r2*dfr/(1-r2) # F-stat
Pcor_prob<-1-pf(Fstat,1,dfr) #p-value calculation
Pcor.prob.adjusted<-p.adjust(Pcor_prob, method=p.adjust.methods, n=length(Pcor_prob)) ##multiple testing correction
significant<-ifelse(Pcor.prob.adjusted<pvalue,Pcor,0) ##above significance threshold
Pcor_results<-cbind(Pcor,Pcor_prob,Pcor.prob.adjusted,significant,ncol(Xsamplesincluded),foldername)
colnames(Pcor_results)<-c("Pcor","Pvalue",p.adjust.methods,"significant","Nsamples_included","Y_variable")
Pcor_results<-cbind(XCMScolumns,Pcor_results)
above_threshold<-which(significant>=Corr.thresh)
above_threshold_dummy<-(significant>=Corr.thresh)*1
Sum_above_threshold<-sum(significant>=Corr.thresh)
Above_threshold_results<-Pcor_results[above_threshold,]
Above_threshold_plots<-Xsamplesincluded[above_threshold,] ######subset for plotting linear regressions
#########################################################################################################################################################
###subset the upper and lower classes of samples for box and whisker plot creation####
if(sum(Yindices[,k])>=round((nrow(Y)*(box.prop)),digits=0)){
high<-as.data.frame(Y[order(Y[,k],decreasing=TRUE)[1:round((nrow(Y)*(box.prop)),digits=0)],k])
row.names(high)<-Yrownames[order(Y[,k],decreasing=TRUE)[1:round((nrow(Y)*(box.prop)),digits=0)]]
colnames(high)<-"Box"
low<-as.data.frame(Y[order(Y[,k],decreasing=FALSE)[1:round((nrow(Y)*(box.prop)),digits=0)],k])
row.names(low)<-Yrownames[order(Y[,k],decreasing=FALSE)[1:round((nrow(Y)*(box.prop)),digits=0)]]
colnames(low)<-"Box"
} else {
high<-as.data.frame(Y[Yindices[,k],k])
row.names(high)<-Yrownames[Yindices[,k]]
colnames(high)<-"Box"
low<-as.data.frame(Y[order(Y[,k],decreasing=FALSE)[1:sum(Yindices[,k])],k])
row.names(low)<-Yrownames[order(Y[,k],decreasing=FALSE)[1:sum(Yindices[,k])]]
colnames(low)<-"Box"
}
high[,1]<-TRUE
low[,1]<-FALSE
YBox<-rbind(high,low)
YBox.row.names<-row.names(YBox)
YBox<-YBox[order(YBox.row.names),]
YBox<-ifelse(YBox==FALSE,"Y.low.zero","Y.high") ###change logical to category names
#########################################################################################################################################################
Above_threshold_Box_plots<-Samples[above_threshold,]
#Quint.df<-t(Samples[,colnames(Samples) %in% food.table$X_DM])
Above_threshold_Box_plots<-as.data.frame(Above_threshold_Box_plots[,colnames(Above_threshold_Box_plots) %in% YBox.row.names])
Above_threshold_Box_plots<-Above_threshold_Box_plots[,order(colnames(Above_threshold_Box_plots))]
Plot_names<-as.character(paste("M",round(Above_threshold_results[,2],digits=4),"T",round(Above_threshold_results[,3],digits=1),sep=""))
#######Plotting above threshold correlation scatterplots#######
if( Sum_above_threshold > 0) {
message(paste("SAVING SCATTER AND BOX AND WHISKER PLOTS...",foldername,sep=""))#,quote=F)
flush.console()
pb<-txtProgressBar(min=0,max=nrow(Above_threshold_plots),style=3)#,width=300)#title="Scatter plot progress bar"
for (i in 1:nrow(Above_threshold_plots)){
###progress bar for plots
Sys.sleep(0.1)
setTxtProgressBar(pb,i)
flush.console()
Titlename<-Plot_names[i]
png(paste(Titlename,".",foldername,".png",sep=""),width=1200,height=1200,res=275)
plot(log10(Yincluded),as.numeric(Above_threshold_plots[i,]), main=paste(foldername,Titlename),sub="(Log)",xlab=paste(foldername,Yunits), ylab="XObs_gLog_QC.LSC",xaxt="n",pch=19,cex=0.6)
axis.labels<-round(lseq(min(Yincluded),max(Yincluded),10),digits=0)
Log10Y<-log10(Yincluded)
axis.points<-seq(min(Log10Y),max(Log10Y),length.out=10)
axis(1, at=axis.points, labels = axis.labels)
graphics.off()
####create box plots####
boxplot.df<-data.frame(Box=YBox,XObs_gLog_QC.LSC=as.numeric(Above_threshold_Box_plots[i,]))
####T TEST HERE#####
zero.boxplot<-boxplot.df[boxplot.df$Box=="Y.low.zero",2]
nonzero.boxplot<-boxplot.df[boxplot.df$Box!="Y.low.zero",2]
b1<-boxplot.stats(zero.boxplot)
b2<-boxplot.stats(nonzero.boxplot)
Above.zero.samples<-paste(round(sum.nonzero.above.zero<-sum(nonzero.boxplot>=b1$stats[[5]]),digits=0)," ","(",round(100*(sum.nonzero.above.zero/length(nonzero.boxplot)),digits=0),"%",")"," ",">1.5*IQR",sep="")
Zero.outliers<-paste(zero.out<-length(which(zero.boxplot>b1$stats[[5]]))," ","(",round(100*(zero.out/length(zero.boxplot)),digits=0),"%",")"," ",">1.5*IQR",sep="") ###number of zero samples above 1.5*IQR threshold
###Box plot score calculation = percentage of non zero samples above zero group penalised by the proportion of samples above zero group whisker in zero group####
box.plot.score<-((sum.nonzero.above.zero/length(nonzero.boxplot))-((zero.out/length(zero.boxplot))))
box.plot.scores<-c(box.plot.scores,box.plot.score)
g<-ggplot(boxplot.df,aes(x=Box,y=XObs_gLog_QC.LSC))+
geom_boxplot(outlier.colour="red",outlier.size=0)+
stat_summary(fun.y=mean, geom="point",shape=18, size=12,col="red",fill="red")+
geom_jitter(position=position_jitter(w=0.15,h=0.15))+
geom_hline(yintercept=b1$stats[[5]],colour="red",size=1)+
annotate("text",label=Above.zero.samples,y=max(nonzero.boxplot)*1.1,x=1,size=3.5)+
annotate("text",label=Zero.outliers,y=max(nonzero.boxplot)*1.1,x=2,size=3.5)+
labs(title=Titlename)+
theme_bw(20)
ggsave(g,filename=paste(Titlename,".",foldername,"_BOXPLOT",".png",sep=""),width = 10, height = 10)
}
message("...Done")#,quote=F)
flush.console()
}
###Features above threshold aggregation#####
Resultscolumns<-rbind(t(Pcor.prob.adjusted),t(Pcor),t(above_threshold_dummy))
rownames(Resultscolumns)<-c(paste(foldername,"_pvalue_",pvalue,"_","MultTest_",p.adjust.methods,sep=""),paste(foldername,"_corr.coeff_nSamples_",ncol(Xsamplesincluded),sep=""),paste(foldername,"_Above_threshold",sep=""))
Biomarker.number<-data.frame(cbind(ncol(Xsamplesincluded),foldername,Sum_above_threshold))
Biomarker_numbers.df<-rbind(Biomarker_numbers.df,Biomarker.number)
above_threshold_df<-rbind(above_threshold_df, t(above_threshold_dummy))
results<-rbind(results,Resultscolumns)
}
}
results<-as.data.frame(t(results))
above_threshold_df<-as.data.frame(t(above_threshold_df))
Results_rowsums<-apply(above_threshold_df,1,sum)
DummyMsignif<-as.data.frame(above_threshold_df[Results_rowsums>0,])
boxplot.score.df<-as.data.frame(matrix(0,ncol=ncol(above_threshold_df),nrow=nrow(DummyMsignif)))
boxplot.score.index<-DummyMsignif==1
###replace dummy matrix with box plot scores for each significant feature###
boxplot.score.df[boxplot.score.index]<-box.plot.scores
colnames(boxplot.score.df)<-paste(Biomarker_numbers.df[,2],rep(".boxplot.score",length.out=nrow(Biomarker_numbers.df)),sep="")
colnames(DummyMsignif)<-as.character(1:ncol(DummyMsignif))
significantmarker_data<-XCMScolumns[Results_rowsums>0,] #subset feature details above threshold
#####Significant Feature HMDB weblink######
if(nrow(significantmarker_data)>2){
significantmarker_mzMED<-significantmarker_data[,mzmed.column.name]
HMDB.url<-data.frame()
for (j in 1:length(significantmarker_mzMED)) {
HMDB.url.link<-as.data.frame(paste("http://www.hmdb.ca/spectra/ms/search?utf8=%E2%9C%93&query_masses=",significantmarker_mzMED[j],"&tolerance=",HMDBtol,"&mode=",mode,"&commit=Search",sep=""))
HMDB.url<-rbind(HMDB.url,HMDB.url.link)
}
colnames(HMDB.url)<-"HMDB.url"
significantmarker_data<-cbind(significantmarker_data,HMDB.url)
}
colnames(significantmarker_data)[1]<-EIC.column.name
correlation.results<-results[Results_rowsums>0,]##sample data minus outliers for column binding following hmdb link generation
Ycorrelated.heatmap<-as.data.frame(correlation.results[,(as.logical(rep(c(0,1,0),length(Y))))])
sample.data<-Samples[Results_rowsums>0,]
##########################################################################################
##########################################################################################
significantmarker_data<-cbind(significantmarker_data,correlation.results,boxplot.score.df,sample.data)
setwd(wd)
####Heatmap######
if (heatmap==TRUE) {
message("Hierarchical clustering and cluster ion identification...PLEASE WAIT")#,quote=F)
flush.console()
RowLabels<-as.character(significantmarker_data[,"name"])
hmcols2<-rev(colorRampPalette(brewer.pal(10,"RdBu"))(256)) ##heatmap colour palette
####Create X-Y correlation heatmap#####
if(ncol(Ycorrelated.heatmap)>1){
#dist.m.method<- function(x) {
#dist.co.x <- 1 - abs(x)
#return(as.dist(dist.co.x))
#}
pdf(paste("HM.Y_X",".pdf"))
heatY<-heatmap.2(t(Ycorrelated.heatmap), dend="column",Colv=TRUE,Rowv=FALSE, symm=FALSE,scale="none",labRow=c(colnames(Ycorrelated.heatmap)),labCol=RowLabels,margins=c(7,7),col=hmcols2,trace="none",key=TRUE,keysize=1.5,cexCol=0.25,cexRow=0.4,hclustfun=function(x) hclust(x,method=hclust.method),distfun=function(x) dist(x,method=dist.m.method))#)
dev.off()
} else if (ncol(Ycorrelated.heatmap)==1){
message("WARNING: only one Y-variable for heatmap creation")
flush.console()
}
###create X-X correlation matrix#####
SignCor<-cor(t(sample.data),method=c("pearson"))
pdf(paste("HM.X_X",".pdf"))
###create X-X correlation matrix#####
heat<-heatmap.2(as.matrix(SignCor), dend="both",Colv=TRUE, symm=TRUE,scale="none",labRow=c(RowLabels),labCol=c(RowLabels),margins=c(2,2),col=hmcols2,trace="none",key=TRUE,cexCol=0.25,cexRow=0.25,hclustfun=function(x) hclust(x,method=hclust.method),distfun=function(x) dist(x,method=dist.m.method))#function(x) hclust(x,method=hclust.method)
dev.off()
###Hierarchical clustering order####
ReorderHierCl<-cbind(as.matrix(seq(1,ncol(SignCor),1)),as.matrix(heat$rowInd))
ReorderHierCl<-ReorderHierCl[order(ReorderHierCl[,2]),]
significantmarker_data<-cbind(ReorderHierCl,significantmarker_data)
colnames(significantmarker_data)[1]<-"Hierclust.order"
significantmarker_data<-significantmarker_data[order(significantmarker_data[,"Hierclust.order"]),]
#####identify retention time clusters#####
rtmed.cluster<-significantmarker_data[,RTmed.column.name]
rtmed.cluster.shift<-c(rtmed.cluster[2],rtmed.cluster[-(length(rtmed.cluster))])
rtmed.difference<-rtmed.cluster-rtmed.cluster.shift
rtmed.cluster.mat<-data.frame(cbind(rtmed.cluster,rtmed.cluster.shift,rtmed.difference))
####match clusters based on similarity in retention time#########
rtmed.cluster.mat$cluster.seq<-ifelse(x<-rtmed.cluster.mat$rtmed.difference<Clust.RT.tol & rtmed.cluster.mat$rtmed.difference>-Clust.RT.tol, cumsum(c(head(x, 1), tail(x, -1) - head(x, -1) == 1)), 0)
rtmed.cluster.mat$cluster.seq<-c(ifelse((head(rtmed.cluster.mat$cluster.seq, -1) + tail(rtmed.cluster.mat$cluster.seq, -1) == tail(rtmed.cluster.mat$cluster.seq, -1)),tail(rtmed.cluster.mat$cluster.seq,-1),head(rtmed.cluster.mat$cluster,-1)),(tail(rtmed.cluster.mat$cluster.seq, 1)))
significantmarker_data<-data.frame(cbind(rtmed.cluster.mat$cluster.seq,significantmarker_data))
colnames(significantmarker_data)[1]<-"mz_clusters"
###order by mz then cluster####
significantmarker_data<-significantmarker_data[order(-significantmarker_data[,"mz_clusters"],significantmarker_data[,mzmed.column.name]),]
###identify mass difference ####
significantmarker_mass.diff<-data.frame(c(0,tail(significantmarker_data$mzmed, -1) - head(significantmarker_data$mzmed, -1)))
significantmarker_mass.diff[significantmarker_data[,"mz_clusters"]==0,]<-0 ###if no cluster replace with zero
significantmarker_data<-cbind((seq(1,nrow(significantmarker_data),1)),significantmarker_data)
colnames(significantmarker_data)[1]<-"cluster_ion_calc.order"
####identify isotopes from mass differences######
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff,2,function(x){ifelse(x<(0.984015583+0.003) & x>(0.984015583-0.003),1,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(0.99703+0.003) & x>(0.99703-0.003),2,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.00336+0.003) & x>(1.00336-0.003),3,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.007825032+0.003) & x>(1.007825032-0.003),4,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.979264556+0.003) & x>(1.979264556-0.003),5,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.9958+0.003) & x>(1.9958-0.003),6,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(2.00425+0.003) & x>(2.00425-0.003),7,x)}))
###remove isotopes before recalculation#####
Isotope_mass.diff.index<-significantmarker_mass.diff.name %in% c(1,2,3,4,5,6,7)
Isotopes<-significantmarker_data[Isotope_mass.diff.index==TRUE,]
Isotope_mass.diff.name<-data.frame(significantmarker_mass.diff[Isotope_mass.diff.index==TRUE,])
Isotope_name<-data.frame(significantmarker_mass.diff.name[Isotope_mass.diff.index==TRUE,])
significantmarker_data<-significantmarker_data[Isotope_mass.diff.index==FALSE,]
significantmarker_mass.diff<-data.frame(c(0,tail(significantmarker_data$mzmed, -1) - head(significantmarker_data$mzmed, -1)))
####replace hierarchical clusters with no retention time connection with a zero####
significantmarker_mass.diff[significantmarker_data[,"mz_clusters"]==0,]<-0 ###if no cluster replace with zero
######recalculate potential isotopes#####
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff,2,function(x){ifelse(x<(0.984015583+0.003) & x>(0.984015583-0.003),1,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(0.99703+0.003) & x>(0.99703-0.003),2,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.00336+0.003) & x>(1.00336-0.003),3,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.007825032+0.003) & x>(1.007825032-0.003),4,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.979264556+0.003) & x>(1.979264556-0.003),5,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(1.9958+0.003) & x>(1.9958-0.003),6,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(2.00425+0.003) & x>(2.00425-0.003),7,x)}))
###remove isotopes again before recalculation of mass differences#####
Isotope_mass.diff.index<-significantmarker_mass.diff.name %in% c(1,2,3,4,5,6,7)
Isotopes<-rbind(Isotopes,significantmarker_data[Isotope_mass.diff.index==TRUE,])
Isotope_mass.diff.name<-rbind(Isotope_mass.diff.name,data.frame(significantmarker_mass.diff[Isotope_mass.diff.index==TRUE,]))
Isotope_name<-rbind(Isotope_name,data.frame(significantmarker_mass.diff.name[Isotope_mass.diff.index==TRUE,]))
significantmarker_data<-significantmarker_data[Isotope_mass.diff.index==FALSE,]
significantmarker_mass.diff<-data.frame(c(0,tail(significantmarker_data$mzmed, -1) - head(significantmarker_data$mzmed, -1)))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff,2,function(x){ifelse(x<(a<-2.015650064)+((Clust.ppm/1000000)*a) & x>(b<-2.015650064)-((Clust.ppm/1000000)*b),8,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-2.999665647)+((Clust.ppm/1000000)*a) & x>(b<-2.999665647)-((Clust.ppm/1000000)*b),9,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-4.031300128)+((Clust.ppm/1000000)*a) & x>(b<-4.031300128)-((Clust.ppm/1000000)*b),10,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-12.0363855)+((Clust.ppm/1000000)*a) & x>(b<-12.0363855)-((Clust.ppm/1000000)*b),11,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-13.97926456)+((Clust.ppm/1000000)*a) & x>(b<-13.97926456)-((Clust.ppm/1000000)*b),12,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-14.01565006)+((Clust.ppm/1000000)*a) & x>(b<-14.01565006)-((Clust.ppm/1000000)*b),13,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-15.0234751)+((Clust.ppm/1000000)*a) & x>(b<-15.0234751)-((Clust.ppm/1000000)*b),14,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-15.01089904)+((Clust.ppm/1000000)*a) & x>(b<-15.01089904)-((Clust.ppm/1000000)*b),15,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-15.99491462)+((Clust.ppm/1000000)*a) & x>(b<-15.99491462)-((Clust.ppm/1000000)*b),16,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-17.0265491)+((Clust.ppm/1000000)*a) & x>(b<-17.0265491)-((Clust.ppm/1000000)*b),17,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-18.01056468)+((Clust.ppm/1000000)*a) & x>(b<-18.01056468)-((Clust.ppm/1000000)*b),18,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-21.981945)+((Clust.ppm/1000000)*a) & x>(b<-21.981945)-((Clust.ppm/1000000)*b),19,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-27.994915)+((Clust.ppm/1000000)*a) & x>(b<-27.994915)-((Clust.ppm/1000000)*b),20,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-28.006148)+((Clust.ppm/1000000)*a) & x>(b<-28.006148)-((Clust.ppm/1000000)*b),21,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-28.0313001)+((Clust.ppm/1000000)*a) & x>(b<-28.0313001)-((Clust.ppm/1000000)*b),22,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-28.9901636)+((Clust.ppm/1000000)*a) & x>(b<-28.9901636)-((Clust.ppm/1000000)*b),23,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-29.0027397)+((Clust.ppm/1000000)*a) & x>(b<-29.0027397)-((Clust.ppm/1000000)*b),24,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-29.0391252)+((Clust.ppm/1000000)*a) & x>(b<-29.0391252)-((Clust.ppm/1000000)*b),25,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-29.97417918)+((Clust.ppm/1000000)*a) & x>(b<-29.97417918)-((Clust.ppm/1000000)*b),26,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-30.01056468)+((Clust.ppm/1000000)*a) & x>(b<-30.01056468)-((Clust.ppm/1000000)*b),27,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-31.018498)+((Clust.ppm/1000000)*a) & x>(b<-31.018498)-((Clust.ppm/1000000)*b),28,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-31.98982924)+((Clust.ppm/1000000)*a) & x>(b<-31.98982924)-((Clust.ppm/1000000)*b),29,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-32.02621475)+((Clust.ppm/1000000)*a) & x>(b<-32.02621475)-((Clust.ppm/1000000)*b),30,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-34.0054793)+((Clust.ppm/1000000)*a) & x>(b<-34.0054793)-((Clust.ppm/1000000)*b),31,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-34.0530982)+((Clust.ppm/1000000)*a) & x>(b<-34.0530982)-((Clust.ppm/1000000)*b),32,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-37.955882)+((Clust.ppm/1000000)*a) & x>(b<-37.955882)-((Clust.ppm/1000000)*b),33,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-41.02669)+((Clust.ppm/1000000)*a) & x>(b<-41.02669)-((Clust.ppm/1000000)*b),34,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-42.010565)+((Clust.ppm/1000000)*a) & x>(b<-42.010565)-((Clust.ppm/1000000)*b),35,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-43.0058137)+((Clust.ppm/1000000)*a) & x>(b<-43.0058137)-((Clust.ppm/1000000)*b),36,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-43.0183897)+((Clust.ppm/1000000)*a) & x>(b<-43.0183897)-((Clust.ppm/1000000)*b),37,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-43.0547752)+((Clust.ppm/1000000)*a) & x>(b<-43.0547752)-((Clust.ppm/1000000)*b),38,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-43.9898292)+((Clust.ppm/1000000)*a) & x>(b<-43.9898292)-((Clust.ppm/1000000)*b),39,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-44.9977)+((Clust.ppm/1000000)*a) & x>(b<-44.9977)-((Clust.ppm/1000000)*b),40,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-46.0419)+((Clust.ppm/1000000)*a) & x>(b<-46.0419)-((Clust.ppm/1000000)*b),41,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-47.98474386)+((Clust.ppm/1000000)*a) & x>(b<-47.98474386)-((Clust.ppm/1000000)*b),42,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-56.0626003)+((Clust.ppm/1000000)*a) & x>(b<-56.0626003)-((Clust.ppm/1000000)*b),43,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-57.02146372)+((Clust.ppm/1000000)*a) & x>(b<-57.02146372)-((Clust.ppm/1000000)*b),44,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-57.0704253)+((Clust.ppm/1000000)*a) & x>(b<-57.0704253)-((Clust.ppm/1000000)*b),45,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-58.0530982)+((Clust.ppm/1000000)*a) & x>(b<-58.0530982)-((Clust.ppm/1000000)*b),46,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-59.0133043)+((Clust.ppm/1000000)*a) & x>(b<-59.0133043)-((Clust.ppm/1000000)*b),47,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-59.0371138)+((Clust.ppm/1000000)*a) & x>(b<-59.0371138)-((Clust.ppm/1000000)*b),48,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-60.0211294)+((Clust.ppm/1000000)*a) & x>(b<-60.0211294)-((Clust.ppm/1000000)*b),49,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-63.961904)+((Clust.ppm/1000000)*a) & x>(b<-63.961904)-((Clust.ppm/1000000)*b),50,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-75.032029)+((Clust.ppm/1000000)*a) & x>(b<-75.032029)-((Clust.ppm/1000000)*b),51,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-78.95850549)+((Clust.ppm/1000000)*a) & x>(b<-78.95850549)-((Clust.ppm/1000000)*b),52,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-79.956819)+((Clust.ppm/1000000)*a) & x>(b<-79.956819)-((Clust.ppm/1000000)*b),53,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-82.0530982)+((Clust.ppm/1000000)*a) & x>(b<-82.0530982)-((Clust.ppm/1000000)*b),54,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-86.000395)+((Clust.ppm/1000000)*a) & x>(b<-86.000395)-((Clust.ppm/1000000)*b),55,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-89.047679)+((Clust.ppm/1000000)*a) & x>(b<-89.047679)-((Clust.ppm/1000000)*b),56,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-97.96737954)+((Clust.ppm/1000000)*a) & x>(b<-97.96737954)-((Clust.ppm/1000000)*b),57,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-103.0091848)+((Clust.ppm/1000000)*a) & x>(b<-103.0091848)-((Clust.ppm/1000000)*b),58,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-107.0040994)+((Clust.ppm/1000000)*a) & x>(b<-107.0040994)-((Clust.ppm/1000000)*b),59,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-119.0041)+((Clust.ppm/1000000)*a) & x>(b<-119.0041)-((Clust.ppm/1000000)*b),60,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-121.019753)+((Clust.ppm/1000000)*a) & x>(b<-121.019753)-((Clust.ppm/1000000)*b),61,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-129.042594)+((Clust.ppm/1000000)*a) & x>(b<-129.042594)-((Clust.ppm/1000000)*b),62,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-146.036779)+((Clust.ppm/1000000)*a) & x>(b<-146.036779)-((Clust.ppm/1000000)*b),63,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-146.069142)+((Clust.ppm/1000000)*a) & x>(b<-146.069142)-((Clust.ppm/1000000)*b),64,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-152.010959)+((Clust.ppm/1000000)*a) & x>(b<-152.010959)-((Clust.ppm/1000000)*b),65,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-161.014668)+((Clust.ppm/1000000)*a) & x>(b<-161.014668)-((Clust.ppm/1000000)*b),66,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-162.052825)+((Clust.ppm/1000000)*a) & x>(b<-162.052825)-((Clust.ppm/1000000)*b),67,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-163.030318)+((Clust.ppm/1000000)*a) & x>(b<-163.030318)-((Clust.ppm/1000000)*b),68,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-176.032088)+((Clust.ppm/1000000)*a) & x>(b<-176.032088)-((Clust.ppm/1000000)*b),69,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-176.047344)+((Clust.ppm/1000000)*a) & x>(b<-176.047344)-((Clust.ppm/1000000)*b),70,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-178.041213)+((Clust.ppm/1000000)*a) & x>(b<-178.041213)-((Clust.ppm/1000000)*b),71,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-180.06339)+((Clust.ppm/1000000)*a) & x>(b<-180.06339)-((Clust.ppm/1000000)*b),72,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-192.027)+((Clust.ppm/1000000)*a) & x>(b<-192.027)-((Clust.ppm/1000000)*b),73,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-194.042655)+((Clust.ppm/1000000)*a) & x>(b<-194.042655)-((Clust.ppm/1000000)*b),74,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-198.014035)+((Clust.ppm/1000000)*a) & x>(b<-198.014035)-((Clust.ppm/1000000)*b),75,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-203.079373)+((Clust.ppm/1000000)*a) & x>(b<-203.079373)-((Clust.ppm/1000000)*b),76,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-215.994605)+((Clust.ppm/1000000)*a) & x>(b<-215.994605)-((Clust.ppm/1000000)*b),77,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-221.089937)+((Clust.ppm/1000000)*a) & x>(b<-221.089937)-((Clust.ppm/1000000)*b),78,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-239.993994)+((Clust.ppm/1000000)*a) & x>(b<-239.993994)-((Clust.ppm/1000000)*b),79,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-255.988909)+((Clust.ppm/1000000)*a) & x>(b<-255.988909)-((Clust.ppm/1000000)*b),80,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-258.004564)+((Clust.ppm/1000000)*a) & x>(b<-258.004564)-((Clust.ppm/1000000)*b),81,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-273.096087)+((Clust.ppm/1000000)*a) & x>(b<-273.096087)-((Clust.ppm/1000000)*b),82,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-273.999479)+((Clust.ppm/1000000)*a) & x>(b<-273.999479)-((Clust.ppm/1000000)*b),83,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-275.111737)+((Clust.ppm/1000000)*a) & x>(b<-275.111737)-((Clust.ppm/1000000)*b),84,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-305.068161)+((Clust.ppm/1000000)*a) & x>(b<-305.068161)-((Clust.ppm/1000000)*b),85,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-307.083811)+((Clust.ppm/1000000)*a) & x>(b<-307.083811)-((Clust.ppm/1000000)*b),86,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-352.06418)+((Clust.ppm/1000000)*a) & x>(b<-352.06418)-((Clust.ppm/1000000)*b),87,x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x<(a<-388.08531)+((Clust.ppm/1000000)*a) & x>(b<-388.08531)-((Clust.ppm/1000000)*b),88,x)}))
#####identify unlabelled cluster differences######
mass.diff.seq<-seq(1,88,1)
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x %in% mass.diff.seq,x,NA)}))
####Name Isotopes######
if(nrow(Isotope_name)>0)
{
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==1,"[+O-NH3]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==2,"[N15 isotope]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==3,"[C13 isotope]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==4,"[+H]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==5,"[+O -CH2]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==6,"[S34 isotope]",x)}))
Isotope_name<-(apply(Isotope_name,2,function(x){ifelse(x==7,"[O18 isotope]",x)}))
}
###Name fragments#####
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==8,"[+H2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==9,"[+OH-N]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==10,"[+H4]/[-H4]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==11,"[+O-C2H4]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==12,"[+O -H2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==13,"[+CH2]/[-CH2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==14,"[-CH3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==15,"[+O2 -NH3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==16,"[Hydroxylation/+O]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==17,"[M+NH4]/[-NH3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==18,"[+H2O]/[-H2O]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==19,"[M+Na]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==20,"[-CO]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==21,"[-N2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==22,"[-C2H4] ",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==23,"[+H-NO] ",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==24,"[-CHO]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==25,"[-C2H5] ",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==26,"[+O2-H2]/[+H2-O2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==27,"[+OCH2]/[-CH2O] ",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==28,"[M+Methanol CH4O]/[-OCH3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==29,"[2 x Hydroxylation]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==30,"[M+MeOH+H]/-[MeOH]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==31,"[+2 OH]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==32,"[M+NH3.NH4]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==33,"[M+K]/-[K]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==34,"[M+MeCN+H]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==35,"[Acetylation shift]/-[AcKetene]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==36,"[-HCNO]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==37,"[-CH3CO]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==38,"[-C3H7]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==39,"[-CO2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==40,"[M+HCOOH]/[-HCOOH/+H-NO2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==41,"[-EtOH]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==42,"[+O3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==43,"[-C4H8]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==44,"[Glycyl conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==45,"[-C4H9]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==46,"[M+MeCN+NH4]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==47,"[-acetate]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==48,"[-Acetamide]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==49,"[-CH3COOH]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==50,"[-SO2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==51,"[-Gly]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==52,"[phosphate - PO3 conjugation]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==53,"[SO3 conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==54,"[M+2ACN+H]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==55,"[-Malonyl]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==56,"[-Cys conj-ala]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==57,"[H2SO4- Sulfate conjugation/phosphate conjugation]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==58,"[Cysteinyl conjugation]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==59,"[Taurine conjugation]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==60,"[S-Cysteine conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==61,"[-Cys]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==62,"[-GSH AnhydroGlu]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==63,"[-Coumaroyl loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==64,"[-Ala-Gly/ GSH Glu loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==65,"[-Galloyl loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==66,"[N-AcetylCysteine conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==67,"[Glucose shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==68,"[-N-AcCys]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==69,"[AnhydroGlucuronide conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==70,"[-Feruloyl loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==71,"[-Cys-Gly loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==72,"[-Gluc loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==73,"[-Hydroxylation+Gluc]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==74,"[-Gluc]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==75,"[-AnhydroGluc + Na]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==76,"[-AnhydroGlucNAc]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==77,"[-Gluc + Na]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==78,"[-GlcNAc loss]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==79,"[-AnhydroGluc + SO2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==80,"[-AnhydroGluc + SO3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==81,"[-Gluc + SO2]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==82,"[-GSH GluAlaGly-2H]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==83,"[-Gluc + SO3]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==84,"[-GSH-GluAlaGly]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==85,"[S-Glutathione conjugation shift]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==86,"[-GSH]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==87,"[-DiAnhydroGlu]",x)}))
significantmarker_mass.diff.name<-(apply(significantmarker_mass.diff.name,2,function(x){ifelse(x==88,"[-DiGlu]",x)}))
###rebind isotopes with potential in-source fragments#####
significantmarker_data<-data.frame(cbind((seq(1,nrow(significantmarker_data),1)),significantmarker_mass.diff.name,significantmarker_mass.diff,significantmarker_data))
Isotopes<-data.frame(cbind((rep(NA,nrow(Isotopes))),Isotope_name,Isotope_mass.diff.name,Isotopes))
colnames(Isotopes)<-colnames(significantmarker_data)
significantmarker_data<-as.data.frame(rbind(significantmarker_data,Isotopes))
colnames(significantmarker_data)[1]<-"Isotope_rem.cluster_calc.order"
colnames(significantmarker_data)[2]<-"Potential_cluster_ions"
colnames(significantmarker_data)[3]<-"cluster_ions_mz.diff"
message("...Done")#,quote=F)
flush.console()
}
significantmarker_data<-significantmarker_data[order(significantmarker_data[,EIC.column.name]),]
###reorder according to mzmed rtmed and ID columns
mz.rt.cols<-which(colnames(significantmarker_data) %in% c(EIC.column.name,mzmed.column.name,RTmed.column.name))
data.cols<-c(1:ncol(significantmarker_data))[-mz.rt.cols]
significantmarker_data<-significantmarker_data[,c(1,mz.rt.cols,data.cols[c(2:length(data.cols))])]
#significantmarker_data<-subset(significantmarker_data,select=-c(X2))
Ycor<-Y
####split significant features into their respective subfolders and save also add column of scatterplot locations#####
for (k in 1:ncol(Ycor)){
foldername<-colnames(Ycor[k]) # new folder name
dirname<-paste(wd,foldername,sep="") #directory name
setwd(dirname) # set working directory to new folder
Yresultsindex<-as.logical(significantmarker_data[,paste(foldername,"_Above_threshold",sep="")])
Yresults.signif<-significantmarker_data[Yresultsindex,]
Above_thresh_csvlabel<-paste(foldername,"_results_above_threshold",".csv",sep="")
if( nrow(Yresults.signif)>1) {
Plot.url.names<-as.character(Yresults.signif[,"name"])
Scatter.url<-data.frame()
for (k in 1:nrow(Yresults.signif)){
Plot.url.Feat.name<-Plot.url.names[k]
Scatter.plot.url<-as.data.frame(paste(wd,foldername,"/",Plot.url.Feat.name,foldername,".png",sep=""))
Scatter.url<-rbind(Scatter.url,Scatter.plot.url)
}
colnames(Scatter.url)<-"Scatter.plot.file.location"
Yresults.signif<-cbind(Yresults.signif,Scatter.url)
write.csv(Yresults.signif,Above_thresh_csvlabel,row.names=FALSE)
} else if (nrow(Yresults.signif)<=1){
Ynocor<-Ycor[,foldername]
setwd(wd)
######save new Y matrix as .csv if Y variables have been removed#####
if (length(ncol(Ynocor)!=ncol(Ycor))!=0) {
write.csv(Ynocor,"Y_uncorrelated_removed.csv")
}
}
}
setwd(wd)
write.csv(significantmarker_data,"Features_Above_threshold.csv",row.names=FALSE)
} else if (ncol(Y)==0) {
print("Error: insufficient Y variable values supplied, decrease non-zero parameter or try others")
}
}
###END###