R/xMSwrapper.XCMS.centWave.R
In kuppal2/xMSanalyzer: xMSanalyzer: R package for data analysis, comparison, and annotation of metabolomics data.
Defines functions
xMSwrapper.XCMS.centWave
Documented in
xMSwrapper.XCMS.centWave
xMSwrapper.XCMS.centWave <-
function(cdfloc, XCMS.outloc,xMSanalyzer.outloc,ppm.list=c(10,25,30), mz.diff.list=c(-0.001,0.1), sn.thresh.list=c(3,5,10), prefilter.list=c(3,100), bw.val=c(10,30),groupval.method="medret",
step.list=c(0.1,1),max=50,minfrac.val=0.5, minsamp.val=2, mzwid.val=0.25, sleep.val=0,retcor.method="obiwarp",retcor.family="symmetric", retcor.plottype="deviation", peakwidth=c(20,50),
numnodes=2,run.order.file=NA,max.mz.diff=15,max.rt.diff=300, merge.eval.pvalue=0.2,mergecorthresh=0.7,deltamzminmax.tol=10,
num_replicates=2,subs=NA, mz.tolerance.dbmatch=10, adduct.list=c("M+H"), samp.filt.thresh=0.70,
feat.filt.thresh=50,cormethod="pearson",mult.test.cor=TRUE,missingvalue=0,ignore.missing=TRUE,
sample_info_file=NA,refMZ=NA,refMZ.mz.diff=10,refMZ.time.diff=NA,void.vol.timethresh=30,replacezeroswithNA=TRUE,scoreweight=30,filepattern=".cdf",charge_type="pos", minexp.pct=0.1,syssleep=0.5)
{
suppressWarnings(sink(file=NULL))
x<-date()
x<-strsplit(x,split=" ")
targeted_feat_raw<-{}
x1<-unlist(x)
#fname<-paste(x1[2:3],x1[5],x1[4],collapse="_")
fname<-paste(x1[2:3],collapse="")
fname<-paste(fname,x1[5],sep="")
x1[4]<-gsub(x1[4],pattern=":",replacement="_")
fname<-paste(fname,x1[4],sep="_")
fname<-paste(xMSanalyzer.outloc,"/Log_",fname,".txt",sep="")
#print(paste("Program running. Please check the logfile for runtime status: ",fname,sep=""))
print(paste("Program is running. Please check the logfile for runtime status: ",fname,sep=""))
data_rpd_all=new("list")
data_rpd=new("list")
union_list=new("list")
feateval_list<-new("list")
rsqres_list<-new("list")
annot.res<-{}
annotres_list<-new("list")
if(is.na(sample_info_file)==FALSE)
{
sampleid_mapping<-read.table(sample_info_file,sep="\t",header=TRUE)
if(is.na(cdfloc)==FALSE){
l1<-list.files(cdfloc,filepattern)
minexp<-minfrac.val*length(l1)
}else{
l1<-rep(1,num_replicates)
}
if(length(l1)!=dim(sampleid_mapping)[1] & (is.na(cdfloc)==FALSE))
{
num_mis_files<-dim(sampleid_mapping)[1]-length(l1)
stop(paste("ERROR: Only ",length(l1)," spectral files were found. ",num_mis_files," files are missing.",sep=""))
}
}else{
if(is.na(cdfloc)==FALSE){
l1<-list.files(cdfloc,filepattern)
minexp<-minfrac.val*length(l1)
}else{
l1<-rep(1,num_replicates)
}
}
if(length(l1)%%num_replicates>0)
{stop(paste("ERROR: Not all samples have ",num_replicates," replicates.",sep=""))
}
############################################
#1) Align profiles using the cdf.to.ftr wrapper function in apLCMS
if(is.na(XCMS.outloc)==TRUE)
{
stop("Undefined value for parameter, XCMS.outloc. Please define the XCMS output location.")
}
if(is.na(xMSanalyzer.outloc)==TRUE)
{
stop("Undefined value for parameter, xMSanalyzer.outloc. Please define the xMSanalyzer output location.")
}
dir.create(XCMS.outloc,showWarnings=FALSE)
dir.create(xMSanalyzer.outloc,showWarnings=FALSE)
sink(fname)
print(sessionInfo())
if(is.na(refMZ)==FALSE){
stddata<-read.table(refMZ,sep="\t",header=TRUE)
print(refMZ)
print(head(stddata))
}else{
if(charge_type=="pos"){
data(example_target_list_pos)
stddata<-example_target_list_pos
}else{
if(charge_type=="neg"){
data(example_target_list_neg)
stddata<-example_target_list_neg
}else{
stop("Invalid option. \'charge_type\' should be \'pos\' or \'neg\'.")
}
}
}
if(is.na(cdfloc)==FALSE)
{
setwd(cdfloc)
if(is.na(XCMS.outloc)==FALSE)
{
# data_rpd_all=XCMS.align.centWave(cdfloc, XCMS.outloc,ppm.list, mz.diff.list, sn.thresh.list, prefilter.list, bw.val,groupval.method,
#step.list,max,minfrac.val, minsamp.val, mzwid.val, sleep.val, run.order.file,subs, retcor.method,retcor.family, retcor.plottype, peakwidth)
data_rpd_all<-XCMS.align.centWave(cdfloc, XCMS.outloc,ppm.list=ppm.list, mz.diff.list=mz.diff.list, sn.thresh.list=sn.thresh.list, prefilter.list=prefilter.list, bw.val=bw.val,groupval.method=groupval.method,
step.list=step.list,max=max,minfrac.val=minfrac.val, minsamp.val=minsamp.val, mzwid.val=mzwid.val, sleep.val=sleep.val, run.order.file,subs, retcor.method=retcor.method,retcor.family=retcor.family, retcor.plottype=retcor.plottype, peakwidth=peakwidth,target.mz.list=stddata)
}
else
{
stop("Undefined value for parameter, XCMS.outloc. Please define the output location.")
}
}
setwd(XCMS.outloc)
alignmentresults<-list.files(XCMS.outloc, "*.txt")
print("Files found in XCMS output location:")
print(alignmentresults)
for(i in 1:length(alignmentresults))
{
data_rpd_all[[i]]<-read.table(paste(XCMS.outloc,"/",alignmentresults[i],sep=""),header=TRUE)
}
#subdir1<-paste(xMSanalyzer.outloc,"/Quality_assessment_files",sep="")
#subdir2<-paste(xMSanalyzer.outloc,"/XCMS_filtered_data",sep="")
#subdir3<-paste(xMSanalyzer.outloc,"/XCMS_with_xMSanalyzer_merged_data",sep="")
#dir.create(subdir1,showWarnings=FALSE)
#dir.create(subdir2,showWarnings=FALSE)
#dir.create(subdir3,showWarnings=FALSE)
subdir1<-paste(xMSanalyzer.outloc,"/Stage1",sep="") #QC individual parameter settings
subdir2<-paste(xMSanalyzer.outloc,"/Stage2",sep="") #Data filtering
subdir3<-paste(xMSanalyzer.outloc,"/Stage3a",sep="") #Data merger/parameter optimization
subdir3b<-paste(xMSanalyzer.outloc,"/Stage3b",sep="")
subdir4a<-paste(xMSanalyzer.outloc,"/Stage4a",sep="") #Raw QC: batch effect eval, TIC, etc
dir.create(subdir1,showWarnings=FALSE)
dir.create(subdir2,showWarnings=FALSE)
dir.create(subdir3,showWarnings=FALSE)
dir.create(subdir3b,showWarnings=FALSE)
dir.create(subdir4a,showWarnings=FALSE)
if(is.na(sample_info_file)==FALSE)
{
subdir4b<-paste(xMSanalyzer.outloc,"/Stage4b",sep="") #Batch-effect corrected QC: batch effect eval, TIC, etc
dir.create(subdir4b,showWarnings=FALSE)
}
if(is.na(adduct.list)==FALSE){
subdir5<-paste(xMSanalyzer.outloc,"/Stage5",sep="") #Putative unprocessed annotations;
dir.create(subdir5,showWarnings=FALSE)
}
bestscore<-(-1000000)
{
#stop("Undefined value for parameter, cdfloc. Please enter path of the folder where the CDF files to be processed are located.")
#change location to the output folder
setwd(XCMS.outloc)
alignmentresults<-list.files(XCMS.outloc, "*.txt")
#alignmentresults<-list.files(XCMS.outloc, pattern="(XCMS).*(bw).*\\.txt")
if(length(data_rpd_all)>0)
{
curdata_dim={}
if(num_replicates==2)
{
fileroot="_PID"
}
else
{
if(num_replicates>2)
{
fileroot="_CV"
}
else
{
fileroot=""
stop("Need at least 2 technical replicates per sample.")
}
}
#for(i in 1:length(alignmentresults))
cat("\n")
print("xMSanalyzer Stage 1: QC evaluation of invidual parameters")
cat("\n")
for(i in 1:length(data_rpd_all))
{
print(paste("******Evaluating XCMS results from parameter setting ",i,"*******",sep=""))
############################################
#2)Calculate pairwise correlation coefficients
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
#curdata=read.table(paste(XCMS.outloc,"/",alignmentresults[i],sep=""),header=TRUE)
#curdata=check.mz.in.replicates(curdata)
curdata=data_rpd_all[[i]]
#############################################
############################################
#3) Calculate Percent Intensity Difference
if(num_replicates>1)
{
feat.eval.result=evaluate.Features(curdata, numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
feat.eval.result<-apply(feat.eval.result,2,as.numeric)
feat.eval.result<-as.data.frame(feat.eval.result)
feat.eval.result.mat=cbind(curdata[,c(1:8)],feat.eval.result)
feat.eval.outfile=paste(subdir1,"/",file_name,fileroot,"featureassessment.txt",sep="")
#write results
write.table(feat.eval.result.mat, feat.eval.outfile,sep="\t", row.names=FALSE)
curdata<-curdata[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),]
feat.eval.result.mat<-feat.eval.result.mat[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),]
#curdata<-cbind(curdata,feat.eval.result[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),])
curdata<-as.data.frame(curdata)
curdata<-replace(as.matrix(curdata),which(is.na(curdata)==TRUE),0)
if(is.na(deltamzminmax.tol)==FALSE){
print("filtering by delta m/z")
mz_min_max<-cbind(curdata[,2],curdata[,3])
mz_min_max<-as.data.frame(mz_min_max)
deltappm_res<-apply(mz_min_max,1,get_deltappm)
curdata<-curdata[which(as.numeric(deltappm_res)<=deltamzminmax.tol),]
feat.eval.result.mat<-feat.eval.result.mat[which(as.numeric(deltappm_res)<=deltamzminmax.tol),]
}
feateval_list[[i]]<-feat.eval.result.mat
data_rpd[[i]]<-curdata
if(num_replicates>1)
{
print(paste("**calculating pairwise ",cormethod," correlation**",sep=""))
rsqres_list<-evaluate.Samples(curdata, num_replicates, alignment.tool="XCMS", cormethod,missingvalue,ignore.missing)
rsqres<-as.data.frame(rsqres_list$cor.matrix)
curdata<-as.data.frame(rsqres_list$feature.table)
rsqres<-as.data.frame(rsqres)
snames<-colnames(curdata[,-c(1:8)])
snames_1<-snames[seq(1,length(snames),num_replicates)]
rownames(rsqres)<-snames_1
pcor_outfile=paste(subdir1,"/",file_name,"_sampleassessment_usinggoodfeatures.txt",sep="")
write.table(rsqres, pcor_outfile,sep="\t",row.names=TRUE)
rsqres_list[[i]]<-rsqres
}
else
{
print("**skipping sample evaluataion as only one replicate is present**")
}
if(num_replicates>2)
{
bad_samples<-which(rsqres$meanCorrelation<samp.filt.thresh)
}else
{
bad_samples<-which(rsqres$Correlation<samp.filt.thresh)
}
if(length(bad_samples)>0){
bad_sample_names<-snames_1[bad_samples]
feat.eval.outfile=paste(subdir1,"/",file_name,"_badsamples_at_cor",samp.filt.thresh,".txt",sep="")
bad_sample_names<-as.data.frame(bad_sample_names)
colnames(bad_sample_names)<-paste("Samples with correlation between technical replicates <", samp.filt.thresh,sep="")
write.table(bad_sample_names, file=feat.eval.outfile,sep="\t", row.names=FALSE)
}
bad_list={}
if(length(bad_samples)>0)
{
for(n1 in 1:length(bad_samples))
{
if(bad_samples[n1]>1)
{
bad_samples[n1]=bad_samples[n1]+(bad_samples[n1]-1)*(num_replicates-1)
}
}
for(n1 in 1:num_replicates)
{
bad_list<-c(bad_list,(bad_samples+n1-1))
}
bad_list<-bad_list[order(bad_list)]
}
if(i>1){
parent_bad_list<-intersect(parent_bad_list,bad_list)
}
else{
parent_bad_list<-bad_list
}
}
else
{
print("*********skipping feature evaluataion as only one replicate is present******")
}
}
cat("\n")
print("********Stage 2: Filtering results from each paramter setting based on sample and feature quality checks*********")
cat("\n")
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
feat.eval.file=paste(subdir2,"/",file_name,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtereddata.txt",sep="")
#data_rpd[[i]]=read.table(feat.eval.file,header=TRUE)
curdata<-data_rpd[[i]]
feat.eval.result.mat<-feateval_list[[i]]
if(length(parent_bad_list)>0){
curdata<-curdata[,-c(parent_bad_list+8)]
#maxint<-apply(curdata[,-c(1:4,((dim(curdata)[2]-6):dim(curdata)[2]))],1,max)
maxint<-apply(curdata[,-c(1:8)],1,max)
badfeats<-which(maxint==0)
if(length(badfeats)>0){
curdata<-curdata[-c(badfeats),]
feat.eval.result.mat<- feat.eval.result.mat[-c(badfeats),]
feateval_list[[i]]<-feat.eval.result.mat
}
}
data_rpd[[i]]<-curdata[which(as.numeric(feat.eval.result.mat$median)<=feat.filt.thresh),]
feat.eval.outfile=paste(subdir2,"/",file_name,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtereddata.txt",sep="")
#write results
write.table(data_rpd[[i]], feat.eval.outfile,sep="\t", row.names=FALSE)
feat.eval.outfile=paste(subdir1,"/",file_name,fileroot,"featureassessment.txt",sep="")
#write results
write.table(feateval_list[[i]], feat.eval.outfile,sep="\t", row.names=FALSE)
}
###########################################
#4) Merge two or more parameter settings
cat("\n")
print("*************Stage 3: merging features detected at different parameter settings********************")
cat("\n")
num_pairs=1
finalres={}
rnames={}
if(length(alignmentresults)>1){
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
bool_num<-1
for(j in i:length(alignmentresults))
{
bool_num<-1
#if(i!=j)
if(i==j){
if(length(alignmentresults)>1){
bool_num<-0
}
else{
bool_num<-1
}
}
#if(i!=j)
if(bool_num==1)
{
file_name=sapply(strsplit(alignmentresults[j],".txt"),head)
# if(i!=j)
{
p1_p2=paste("p",i,"_U_","p",j,sep="")
}
# else
#{
# p1_p2="p1"
#}
feat.eval.A<-feateval_list[[i]]
feat.eval.B<-feateval_list[[j]]
feat.eval.A<-feat.eval.A[which(as.numeric(feat.eval.A$median)<=feat.filt.thresh),]
feat.eval.B<-feat.eval.B[which(as.numeric(feat.eval.B$median)<=feat.filt.thresh),]
#print(p1_p2)
print(paste("Number of good quality features from setting ",i,":", dim(data_rpd[[i]])[1],sep=": "))
print(paste("Number of good quality features from setting ",j,":",dim(data_rpd[[j]])[1],sep=": "))
data_m=merge.Results(data_rpd[[i]],data_rpd[[j]],feat.eval.A,feat.eval.B,max.mz.diff,max.rt.diff, merge.eval.pvalue,alignment.tool="XCMS",
numnodes=numnodes, mult.test.cor,mergecorthresh,missingvalue)
numcols<-dim(data_m)[2]
data_m_int<-data_m[,-c(1:8,(numcols-7):numcols)]
numsamps<-dim(data_m_int)[2]/num_replicates
maxint<-apply(data_m_int,1,function(x){max(x,na.rm=TRUE)})
numpeaks<-lapply(1:dim(data_m_int)[1],function(j){length(which(is.na(data_m_int[j,])==FALSE))})
data_m[,8]<-numpeaks
if(is.na(minexp.pct)==FALSE)
{
minexp<-minexp.pct*dim(data_m_int)[2]
if(length(which(data_m[,8]>=minexp))>0){
data_m<-data_m[which(data_m[,8]>=minexp),]
}else{
stop(paste("No features have non-missing value in ",minexp, " samples",sep=""))
}
}
union_list[[num_pairs]]<-data_m[,c(1:8)]
#union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],numpeaks)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$numgoodsamples)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$median)
#Qscore<-(as.numeric(data_m$numgoodsamples)/as.numeric(data_m$median+0.1))
#Qscore<-100*(Qscore/numsamps)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$Qscore)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],maxint)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m_int)
featinfo<-colnames(data_m[,c(1:7)])
cnames<-colnames(data_m_int)
merge.res.colnames<-c(featinfo,"NumPres.All.Samples","NumPres.Biological.Samples",paste("median",fileroot,sep=""),"Qscore","Max.Intensity",cnames)
colnames(union_list[[num_pairs]])<-as.character(merge.res.colnames)
curres={}
curres=cbind(curres, p1_p2)
curres=cbind(curres, dim(union_list[[num_pairs]])[1])
curres=cbind(curres, mean(as.numeric(data_m$median)))
curres=cbind(curres, mean(as.numeric(data_m$Qscore)))
# curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$Qscore))))
curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$median))))
if(curscore>bestscore){
bestscore<-curscore
best_i<-num_pairs
best_pair<-p1_p2
}
curres=cbind(curres,curscore)
curres<-as.data.frame(curres)
finalres=rbind(finalres,curres)
finalname=paste("XCMS_feature_list_at_", p1_p2,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
#Output merge results
write.table(union_list[[num_pairs]],file=paste(subdir3,"/",finalname,sep=""), sep="\t",row.names=FALSE)
num_pairs=num_pairs+1
}
}
}
}else{
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
j=i
{
if(i==j)
{
file_name=sapply(strsplit(alignmentresults[j],".txt"),head)
# if(i!=j)
{
p1_p2=paste("p",i,"_U_","p",j,sep="")
}
# else
#{
# p1_p2="p1"
#}
feat.eval.A<-feateval_list[[i]]
feat.eval.B<-feateval_list[[j]]
feat.eval.A<-feat.eval.A[which(as.numeric(feat.eval.A$median)<=feat.filt.thresh),]
feat.eval.B<-feat.eval.B[which(as.numeric(feat.eval.B$median)<=feat.filt.thresh),]
#print(p1_p2)
print(paste("Number of good quality features from setting ",i,":", dim(data_rpd[[i]])[1],sep=": "))
#print(paste("Number of good quality features from setting ",j,":",dim(data_rpd[[j]])[1],sep=": "))
data_m=merge.Results(data_rpd[[i]],data_rpd[[j]],feat.eval.A,feat.eval.B,max.mz.diff,max.rt.diff, merge.eval.pvalue,alignment.tool="XCMS",
numnodes=numnodes, mult.test.cor,mergecorthresh,missingvalue)
numcols<-dim(data_m)[2]
data_m_int<-data_m[,-c(1:8,(numcols-7):numcols)]
numsamps<-dim(data_m_int)[2]/num_replicates
maxint<-apply(data_m_int,1,function(x){max(x,na.rm=TRUE)})
numpeaks<-lapply(1:dim(data_m_int)[1],function(j){length(which(is.na(data_m_int[j,])==FALSE))})
data_m[,8]<-numpeaks
if(is.na(minexp.pct)==FALSE){
minexp<-minexp.pct*dim(data_m_int)[2]
if(length(which(data_m[,8]>=minexp))>0){
data_m<-data_m[which(data_m[,8]>=minexp),]
}else{
stop(paste("No features have non-missing value in ",minexp, " samples",sep=""))
}
}
union_list[[num_pairs]]<-data_m[,c(1:8)]
#union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],numpeaks)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$numgoodsamples)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$median)
#Qscore<-(as.numeric(data_m$numgoodsamples)/as.numeric(data_m$median+0.1))
#Qscore<-100*(Qscore/numsamps)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$Qscore)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],maxint)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m_int)
featinfo<-colnames(data_m[,c(1:7)])
cnames<-colnames(data_m_int)
merge.res.colnames<-c(featinfo,"NumPres.All.Samples","NumPres.Biological.Samples",paste("median",fileroot,sep=""),"Qscore","Max.Intensity",cnames)
colnames(union_list[[num_pairs]])<-as.character(merge.res.colnames)
curres={}
curres=cbind(curres, p1_p2)
curres=cbind(curres, dim(union_list[[num_pairs]])[1])
curres=cbind(curres, mean(as.numeric(data_m$median)))
curres=cbind(curres, mean(as.numeric(data_m$Qscore)))
# curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$Qscore))))
curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$median))))
if(curscore>bestscore){
bestscore<-curscore
best_i<-num_pairs
best_pair<-p1_p2
}
curres=cbind(curres,curscore)
curres<-as.data.frame(curres)
finalres=rbind(finalres,curres)
finalname=paste("XCMS_feature_list_at_", p1_p2,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
#Output merge results
write.table(union_list[[num_pairs]],file=paste(subdir3,"/",finalname,sep=""), sep="\t",row.names=FALSE)
metlin.res={}
kegg.res={}
#length(union_list[[num_pairs]]$mz
num_pairs=num_pairs+1
}
}
}
}
finalres<-as.data.frame(finalres)
colnames(finalres)<-c("Parameter Combination", "Number of Features", "median PID/CV between sample replicates", "mean Qscore (Quality score)", "Parameter score")
write.table(finalres,file=paste(xMSanalyzer.outloc,"/Stage3a/xcms_with_xMSanalyzer_merge_summary.txt",sep=""), sep="\t", row.names=FALSE)
print("Most optimal feature setting:")
print(best_pair)
cat("\n")
#########################################################################
#rawQCeval/
cat("\n")
print(paste("********Stage 3b: Generating final (pre-batcheffect correction) untargeted and targeted feature tables using ",best_pair," results******",sep=""))
cat("\n")
# pdf("Stage4a_QC_plots.pdf")
#pdf(file=paste("subdir4a/Stage4a_QC_plots.pdf",sep=""))
pdf(file=paste(subdir4a,"/Stage4a_QC_plots.pdf",sep=""))
#most optimal set after merger
d1<-union_list[[best_i]]
if(is.na(refMZ)==FALSE){
stddata<-read.table(refMZ,sep="\t",header=TRUE)
print(refMZ)
print(head(stddata))
}else{
if(charge_type=="pos"){
data(example_target_list_pos)
stddata<-example_target_list_pos
}else{
if(charge_type=="neg"){
data(example_target_list_neg)
stddata<-example_target_list_neg
}else{
stop("Invalid option. \'charge_type\' should be \'pos\' or \'neg\'.")
}
}
}
Sys.sleep(1)
hist(d1$NumPres.All.Samples,main="Histogram NumPres.All.Samples",col="orange",xlab="Number of samples (including replicates) \n with non-zero intensity values", ylab="Number of features",cex.main=0.7)
hist(d1$NumPres.Biological.Samples,main="Histogram NumPres.Biological.Samples \n (using all data)",col="orange",xlab="Number of biological samples \n with non-zero intensity values", ylab="Number of features",cex.main=0.7)
#h1<-hist(d1$median_CV,main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
if(num_replicates>2){
h1<-hist(d1$median_CV,breaks=seq(0,max(d1$median_CV,na.rm=TRUE)+10,10),main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
}else{
h1<-hist(d1$median_PID,breaks=seq(0,max(d1$median_PID,na.rm=TRUE)+10,10),main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
}
hist(d1$Qscore,main="Histogram Qscore \n (using all data)",col="orange",xlab="Quality score", ylab="Number of features",cex.main=0.7)
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=h1$breaks,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=h1$breaks,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
lab_text<-paste(h1$breaks,"-",h1$breaks+10,sep="")
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
if(num_replicates>2){
pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main=paste("Pie chart of median CVs (%) \n using all features\n; average=",mean(d1$median_CV),sep=""),cex.main=0.7)
}else{
pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main=paste("Pie chart of median PIDs (%) \n using all features\n; average=",mean(d1$median_PID),sep=""),cex.main=0.7)
}
d2<-d1[order(d1$time),]
plot(d2$time,d2$Max.Intensity,main="TIC \n (using all data)",col="orange",xlab="Time (s)",ylab="Max intensity across all samples/profiles",cex.main=0.7)
plot(d2$time,d2$mz,main="m/z vs Time \n (using all data)",col="orange",xlab="Time (s)",ylab="m/z",cex.main=0.7)
plot(d2$time,d2$Qscore,main="Qscore vs Time \n (using all data)",col="orange",xlab="Time (s)",ylab="Qscore",cex.main=0.7)
plot(d2$mz,d2$Qscore,main="Qscore vs m/z \n (using all data)",col="orange",xlab="m/z",ylab="Qscore",cex.main=0.7)
max_numzeros<-dim(d1)[2]*1
if(is.na(void.vol.timethresh)==FALSE){
dfirst15<-d1[which(d1[,2]<void.vol.timethresh),]
if(nrow(dfirst15)>1){
print(dfirst15)
ind1<-which(dfirst15[,12]==max(dfirst15[,12]))
time_thresh<-dfirst15[ind1,2]
time_thresh<-time_thresh-(0.30*time_thresh)
# plot(dfirst15[,2],dfirst15[,9],xlab="Time (s)", ylab="Max intensity across all samples")
plot(dfirst15[,2],dfirst15[,9],xlab="Time (s)", ylab="Max intensity across all samples", main=paste("Estimated void volume time: ",time_thresh," s",sep=""))
abline(v=time_thresh,col=4,lty=3)
d1<-d1[which(d1$time>=time_thresh),]
print("Estimated void volume time cutoff")
print(time_thresh)
}
d1_int<-round(d1[,-c(1:12)],0)
d1<-cbind(d1[,c(1:12)],d1_int)
rm(d1_int)
#sfname<-paste(xMSanalyzer.outloc,"/stage5/feature_table_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtered.txt",sep="")
finalname<-paste("featuretable_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,"_voidtimefilt.txt",sep="")
write.table(d1,file=paste(subdir3b,"/",finalname,sep=""),sep="\t",row.names=FALSE)
}else{
finalname<-paste("featuretable_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
write.table(d1,file=paste(subdir3b,"/",finalname,sep=""),sep="\t",row.names=FALSE)
}
cat("\n")
print(paste("********Stage 4a: Performing QC checks using ",best_pair," results*******",sep=""))
cat("\n")
Sys.sleep(1)
print("Dim data after void time filtering")
print(dim(d1))
data_m<-d1[,-c(1:12)]
if(replacezeroswithNA==TRUE){
data_m<-replace(as.matrix(data_m),which(data_m==0),NA)
}
counna<-apply(data_m,1,function(x){length(which(is.na(x)==TRUE))})
maxzeros<-1*dim(data_m)[2]
if(length(which(counna<maxzeros))){
data_m<-data_m[which(counna<maxzeros),]
}
maxint<-apply(data_m,1,function(x){max(x,na.rm=TRUE)})
maxint_ord<-order(maxint,decreasing=TRUE)
#[maxint_ord[1:5000]
X<-t(data_m) #[maxint_ord[1:2000],])
X<-replace(as.matrix(X),which(is.na(X)==TRUE),0)
tic.eval(d1[,-c(1:12)],outloc=subdir4a)
feat.eval.result=evaluate.Features(d1[,-c(9:12)], numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
Sys.sleep(1)
s1<-"Stage 1 results: QC evaluation of invidual parameters from apLCMS"
s2<-"Stage 2 results: filtered results from each paramter setting based on sample and feature quality (CV within replicates) checks"
s3<-"Stage 3a results: merged results using stage 2 filtered files"
s3b<-"Stage 3b results: Final untargeted and targeted feature tables"
s4a<-"Stage 4a results: QC evaluation of targeted and untargeted data before batch-effect correction"
sm<-rbind(s1,s2,s3,s3b,s4a)
targeted_feat_raw<-eval.target.mz(dataA=d1[,-c(3:12)],refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=refMZ.mz.diff,timethresh=refMZ.time.diff,outloc=subdir4a,folderheader="raw")
if(is.na(sample_info_file)==FALSE)
{
#sampleid_mapping<-read.table(sample_info_file,sep="\t",header=TRUE)
sampleid_mapping[,1]<-gsub(sampleid_mapping[,1],pattern=filepattern,replacement="")
cnames<-colnames(data_m)
cnames<-gsub(cnames,pattern=filepattern,replacement="")
colnames(data_m)<-cnames
batch_inf<-sampleid_mapping[,3]
batch_labels<-as.factor(sampleid_mapping[,3])
l1<-levels(batch_labels)
pca.eval(X=X,samplelabels=batch_labels,filename="raw",ncomp=5,center=TRUE,scale=TRUE,legendlocation="topright",legendcex=0.5,outloc=subdir4a)
Sys.sleep(1)
if(dim(sampleid_mapping)[2]<4){
mod<-rep(1,dim(data_m)[2])
}else{
mod<-sampleid_mapping[,-c(1:3)]
}
dev.off()
Sys.sleep(1)
#######################################################################
cat("\n")
print("Stage 4b: Performing batch-effect correction and post-correction QC evaluation")
cat("\n")
# pdf("Stage4b_QC_plots.pdf")
#pdf(file=paste("subdir4b/Stage4b_QC_plots.pdf",sep=""))
pdf(file=paste(subdir4b,"/Stage4b_QC_plots.pdf",sep=""))
##################################################################
adjdata<-try(sva::ComBat(dat=data_m,batch=batch_inf,mod=mod,par.prior=TRUE),silent=TRUE)
if(is(adjdata,"try-error")){
data_m1<-cbind(d1[,c(1:4)],data_m)
#print(adjdata)
print("sva::ComBat caused an error.")
print(adjdata)
print("Too many missing values can cause this error. Trying MetabComBat...")
#adjdata<-MetabComBat(dat=data_m1,saminfo=sampleid_mapping,par.prior=T,filter=F,write=F)
adjdata<-MetabComBat(dat=data_m1,saminfo=sampleid_mapping,par.prior=T,filter=F,write=F,prior.plots=F)
adjdata<-adjdata[,-c(1:4)]
}
maxint<-apply(adjdata,1,function(x){max(x,na.rm=TRUE)})
adjdata2<-replace(as.matrix(adjdata),which(is.na(adjdata)==TRUE),0)
adjdata2<-replace(as.matrix(adjdata2),which(adjdata2<0),0)
adjdata2<-cbind(d1[,c(1:8)],adjdata2)
expression_xls<-paste(subdir4b,"/ComBat_corrected_",best_pair,"_feature_table.txt",sep="")
feat.eval.result=evaluate.Features(adjdata2, numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
feat.eval.result<-apply(feat.eval.result,2,as.numeric)
feat.eval.result<-as.data.frame(feat.eval.result)
feat.eval.result.mat=cbind(adjdata2[,c(1:4)],feat.eval.result)
numpeaks<-apply(adjdata2[,-c(1:8)],1,countpeaks)
maxint<-apply(adjdata2[,-c(1:8)],1,function(x){max(x,na.rm=TRUE)})
adjdata2<-cbind(adjdata2[,c(1:7)],numpeaks,feat.eval.result.mat$numgoodsamples,feat.eval.result.mat$median,feat.eval.result.mat$Qscore,maxint,adjdata2[,-c(1:8)])
colnames(adjdata2)<-colnames(d1)
write.table(adjdata2,file=paste(subdir4b,"/",expression_xls,sep=""),sep="\t",row.names=FALSE)
# X<-t(adjdata2[maxint_ord[1:2000],-c(1:9)])
tic.eval(adjdata2[,-c(1:12)],outloc=subdir4b)
X<-t(adjdata2[,-c(1:12)])
X<-as.matrix(X)
X<-replace(as.matrix(X),which(is.na(X)==TRUE),0)
pca.eval(X,samplelabels=batch_labels,filename="ComBat",ncomp=5,center=TRUE,scale=TRUE,legendlocation="topright",legendcex=0.5,outloc=subdir4b)
#eval.reference.metabs(dataA=adjdata2,stdData=stddata,mzthresh=10,outloc=getwd(),folderheader="ComBat")
#eval.target.mz(dataA=adjdata2,refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=10,outloc=subdir4b,folderheader="ComBat")
targeted_feat_combat<-eval.target.mz(dataA=adjdata2[,-c(3:12)],refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=refMZ.mz.diff,timethresh=refMZ.time.diff,outloc=subdir4b,folderheader="ComBat")
s4b<-"Stage 4b results: Batch-effect evaluation post ComBat and QC evaluation of targeted data post batch-effect correction"
sm<-rbind(sm,s4b)
}else{
adjdata2=NULL
targeted_feat_combat<-NULL
}
dev.off()
metlin.res={}
kegg.res={}
#length(union_list[[num_pairs]]$mz
if(is.na(adduct.list)==FALSE){
cat("\n")
print("*********Stage 5: Mapping m/z values to known metabolites using KEGG*********")
cat("\n")
annot.res<-feat.batch.annotation.KEGG(adjdata2,mz.tolerance.dbmatch,adduct.list,subdir5, numnodes=numnodes,syssleep=syssleep)
annotres_list<-annot.res
s5<-"Stage 5 results: Annotation of features"
sm<-rbind(sm,s5)
}
print("*************Processing complete**********")
#print("*********Characterizing metabolites*********")
}
else
{
stop(paste("No files exist in",XCMS.outloc, "Please check the input value for cdfloc", sep=""))
}
}
suppressWarnings(sink(file=NULL))
sm<-as.data.frame(sm)
colnames(sm)<-"Output_description"
setwd(xMSanalyzer.outloc)
write.table(sm,file="Readme.txt",sep="\t",row.names=FALSE)
print(paste("Processing is complete. Program results can be found at: ",xMSanalyzer.outloc,sep=""))
return(list("XCMS.merged.res"=union_list, "XCMS.ind.res"=data_rpd_all,"XCMS.ind.res.filtered"=data_rpd, "final.feat.table.annot"=annotres_list, "feat.eval.ind"=feateval_list, "sample.eval.ind"=rsqres_list,"final.feat.table.raw"=d1,"final.feat.table.combat"=adjdata2,
"final.targeted.feat.table.raw"=targeted_feat_raw,"final.targeted.feat.table.combat"=targeted_feat_combat))
}
kuppal2/xMSanalyzer documentation built on Feb. 12, 2021, 12:36 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions xMSwrapper.XCMS.centWave
Documented in xMSwrapper.XCMS.centWave
xMSwrapper.XCMS.centWave <-
function(cdfloc, XCMS.outloc,xMSanalyzer.outloc,ppm.list=c(10,25,30), mz.diff.list=c(-0.001,0.1), sn.thresh.list=c(3,5,10), prefilter.list=c(3,100), bw.val=c(10,30),groupval.method="medret",
step.list=c(0.1,1),max=50,minfrac.val=0.5, minsamp.val=2, mzwid.val=0.25, sleep.val=0,retcor.method="obiwarp",retcor.family="symmetric", retcor.plottype="deviation", peakwidth=c(20,50),
numnodes=2,run.order.file=NA,max.mz.diff=15,max.rt.diff=300, merge.eval.pvalue=0.2,mergecorthresh=0.7,deltamzminmax.tol=10,
num_replicates=2,subs=NA, mz.tolerance.dbmatch=10, adduct.list=c("M+H"), samp.filt.thresh=0.70,
feat.filt.thresh=50,cormethod="pearson",mult.test.cor=TRUE,missingvalue=0,ignore.missing=TRUE,
sample_info_file=NA,refMZ=NA,refMZ.mz.diff=10,refMZ.time.diff=NA,void.vol.timethresh=30,replacezeroswithNA=TRUE,scoreweight=30,filepattern=".cdf",charge_type="pos", minexp.pct=0.1,syssleep=0.5)
{
suppressWarnings(sink(file=NULL))
x<-date()
x<-strsplit(x,split=" ")
targeted_feat_raw<-{}
x1<-unlist(x)
#fname<-paste(x1[2:3],x1[5],x1[4],collapse="_")
fname<-paste(x1[2:3],collapse="")
fname<-paste(fname,x1[5],sep="")
x1[4]<-gsub(x1[4],pattern=":",replacement="_")
fname<-paste(fname,x1[4],sep="_")
fname<-paste(xMSanalyzer.outloc,"/Log_",fname,".txt",sep="")
#print(paste("Program running. Please check the logfile for runtime status: ",fname,sep=""))
print(paste("Program is running. Please check the logfile for runtime status: ",fname,sep=""))
data_rpd_all=new("list")
data_rpd=new("list")
union_list=new("list")
feateval_list<-new("list")
rsqres_list<-new("list")
annot.res<-{}
annotres_list<-new("list")
if(is.na(sample_info_file)==FALSE)
{
sampleid_mapping<-read.table(sample_info_file,sep="\t",header=TRUE)
if(is.na(cdfloc)==FALSE){
l1<-list.files(cdfloc,filepattern)
minexp<-minfrac.val*length(l1)
}else{
l1<-rep(1,num_replicates)
}
if(length(l1)!=dim(sampleid_mapping)[1] & (is.na(cdfloc)==FALSE))
{
num_mis_files<-dim(sampleid_mapping)[1]-length(l1)
stop(paste("ERROR: Only ",length(l1)," spectral files were found. ",num_mis_files," files are missing.",sep=""))
}
}else{
if(is.na(cdfloc)==FALSE){
l1<-list.files(cdfloc,filepattern)
minexp<-minfrac.val*length(l1)
}else{
l1<-rep(1,num_replicates)
}
}
if(length(l1)%%num_replicates>0)
{stop(paste("ERROR: Not all samples have ",num_replicates," replicates.",sep=""))
}
############################################
#1) Align profiles using the cdf.to.ftr wrapper function in apLCMS
if(is.na(XCMS.outloc)==TRUE)
{
stop("Undefined value for parameter, XCMS.outloc. Please define the XCMS output location.")
}
if(is.na(xMSanalyzer.outloc)==TRUE)
{
stop("Undefined value for parameter, xMSanalyzer.outloc. Please define the xMSanalyzer output location.")
}
dir.create(XCMS.outloc,showWarnings=FALSE)
dir.create(xMSanalyzer.outloc,showWarnings=FALSE)
sink(fname)
print(sessionInfo())
if(is.na(refMZ)==FALSE){
stddata<-read.table(refMZ,sep="\t",header=TRUE)
print(refMZ)
print(head(stddata))
}else{
if(charge_type=="pos"){
data(example_target_list_pos)
stddata<-example_target_list_pos
}else{
if(charge_type=="neg"){
data(example_target_list_neg)
stddata<-example_target_list_neg
}else{
stop("Invalid option. \'charge_type\' should be \'pos\' or \'neg\'.")
}
}
}
if(is.na(cdfloc)==FALSE)
{
setwd(cdfloc)
if(is.na(XCMS.outloc)==FALSE)
{
# data_rpd_all=XCMS.align.centWave(cdfloc, XCMS.outloc,ppm.list, mz.diff.list, sn.thresh.list, prefilter.list, bw.val,groupval.method,
#step.list,max,minfrac.val, minsamp.val, mzwid.val, sleep.val, run.order.file,subs, retcor.method,retcor.family, retcor.plottype, peakwidth)
data_rpd_all<-XCMS.align.centWave(cdfloc, XCMS.outloc,ppm.list=ppm.list, mz.diff.list=mz.diff.list, sn.thresh.list=sn.thresh.list, prefilter.list=prefilter.list, bw.val=bw.val,groupval.method=groupval.method,
step.list=step.list,max=max,minfrac.val=minfrac.val, minsamp.val=minsamp.val, mzwid.val=mzwid.val, sleep.val=sleep.val, run.order.file,subs, retcor.method=retcor.method,retcor.family=retcor.family, retcor.plottype=retcor.plottype, peakwidth=peakwidth,target.mz.list=stddata)
}
else
{
stop("Undefined value for parameter, XCMS.outloc. Please define the output location.")
}
}
setwd(XCMS.outloc)
alignmentresults<-list.files(XCMS.outloc, "*.txt")
print("Files found in XCMS output location:")
print(alignmentresults)
for(i in 1:length(alignmentresults))
{
data_rpd_all[[i]]<-read.table(paste(XCMS.outloc,"/",alignmentresults[i],sep=""),header=TRUE)
}
#subdir1<-paste(xMSanalyzer.outloc,"/Quality_assessment_files",sep="")
#subdir2<-paste(xMSanalyzer.outloc,"/XCMS_filtered_data",sep="")
#subdir3<-paste(xMSanalyzer.outloc,"/XCMS_with_xMSanalyzer_merged_data",sep="")
#dir.create(subdir1,showWarnings=FALSE)
#dir.create(subdir2,showWarnings=FALSE)
#dir.create(subdir3,showWarnings=FALSE)
subdir1<-paste(xMSanalyzer.outloc,"/Stage1",sep="") #QC individual parameter settings
subdir2<-paste(xMSanalyzer.outloc,"/Stage2",sep="") #Data filtering
subdir3<-paste(xMSanalyzer.outloc,"/Stage3a",sep="") #Data merger/parameter optimization
subdir3b<-paste(xMSanalyzer.outloc,"/Stage3b",sep="")
subdir4a<-paste(xMSanalyzer.outloc,"/Stage4a",sep="") #Raw QC: batch effect eval, TIC, etc
dir.create(subdir1,showWarnings=FALSE)
dir.create(subdir2,showWarnings=FALSE)
dir.create(subdir3,showWarnings=FALSE)
dir.create(subdir3b,showWarnings=FALSE)
dir.create(subdir4a,showWarnings=FALSE)
if(is.na(sample_info_file)==FALSE)
{
subdir4b<-paste(xMSanalyzer.outloc,"/Stage4b",sep="") #Batch-effect corrected QC: batch effect eval, TIC, etc
dir.create(subdir4b,showWarnings=FALSE)
}
if(is.na(adduct.list)==FALSE){
subdir5<-paste(xMSanalyzer.outloc,"/Stage5",sep="") #Putative unprocessed annotations;
dir.create(subdir5,showWarnings=FALSE)
}
bestscore<-(-1000000)
{
#stop("Undefined value for parameter, cdfloc. Please enter path of the folder where the CDF files to be processed are located.")
#change location to the output folder
setwd(XCMS.outloc)
alignmentresults<-list.files(XCMS.outloc, "*.txt")
#alignmentresults<-list.files(XCMS.outloc, pattern="(XCMS).*(bw).*\\.txt")
if(length(data_rpd_all)>0)
{
curdata_dim={}
if(num_replicates==2)
{
fileroot="_PID"
}
else
{
if(num_replicates>2)
{
fileroot="_CV"
}
else
{
fileroot=""
stop("Need at least 2 technical replicates per sample.")
}
}
#for(i in 1:length(alignmentresults))
cat("\n")
print("xMSanalyzer Stage 1: QC evaluation of invidual parameters")
cat("\n")
for(i in 1:length(data_rpd_all))
{
print(paste("******Evaluating XCMS results from parameter setting ",i,"*******",sep=""))
############################################
#2)Calculate pairwise correlation coefficients
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
#curdata=read.table(paste(XCMS.outloc,"/",alignmentresults[i],sep=""),header=TRUE)
#curdata=check.mz.in.replicates(curdata)
curdata=data_rpd_all[[i]]
#############################################
############################################
#3) Calculate Percent Intensity Difference
if(num_replicates>1)
{
feat.eval.result=evaluate.Features(curdata, numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
feat.eval.result<-apply(feat.eval.result,2,as.numeric)
feat.eval.result<-as.data.frame(feat.eval.result)
feat.eval.result.mat=cbind(curdata[,c(1:8)],feat.eval.result)
feat.eval.outfile=paste(subdir1,"/",file_name,fileroot,"featureassessment.txt",sep="")
#write results
write.table(feat.eval.result.mat, feat.eval.outfile,sep="\t", row.names=FALSE)
curdata<-curdata[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),]
feat.eval.result.mat<-feat.eval.result.mat[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),]
#curdata<-cbind(curdata,feat.eval.result[which(as.numeric(feat.eval.result$median)<=feat.filt.thresh),])
curdata<-as.data.frame(curdata)
curdata<-replace(as.matrix(curdata),which(is.na(curdata)==TRUE),0)
if(is.na(deltamzminmax.tol)==FALSE){
print("filtering by delta m/z")
mz_min_max<-cbind(curdata[,2],curdata[,3])
mz_min_max<-as.data.frame(mz_min_max)
deltappm_res<-apply(mz_min_max,1,get_deltappm)
curdata<-curdata[which(as.numeric(deltappm_res)<=deltamzminmax.tol),]
feat.eval.result.mat<-feat.eval.result.mat[which(as.numeric(deltappm_res)<=deltamzminmax.tol),]
}
feateval_list[[i]]<-feat.eval.result.mat
data_rpd[[i]]<-curdata
if(num_replicates>1)
{
print(paste("**calculating pairwise ",cormethod," correlation**",sep=""))
rsqres_list<-evaluate.Samples(curdata, num_replicates, alignment.tool="XCMS", cormethod,missingvalue,ignore.missing)
rsqres<-as.data.frame(rsqres_list$cor.matrix)
curdata<-as.data.frame(rsqres_list$feature.table)
rsqres<-as.data.frame(rsqres)
snames<-colnames(curdata[,-c(1:8)])
snames_1<-snames[seq(1,length(snames),num_replicates)]
rownames(rsqres)<-snames_1
pcor_outfile=paste(subdir1,"/",file_name,"_sampleassessment_usinggoodfeatures.txt",sep="")
write.table(rsqres, pcor_outfile,sep="\t",row.names=TRUE)
rsqres_list[[i]]<-rsqres
}
else
{
print("**skipping sample evaluataion as only one replicate is present**")
}
if(num_replicates>2)
{
bad_samples<-which(rsqres$meanCorrelation<samp.filt.thresh)
}else
{
bad_samples<-which(rsqres$Correlation<samp.filt.thresh)
}
if(length(bad_samples)>0){
bad_sample_names<-snames_1[bad_samples]
feat.eval.outfile=paste(subdir1,"/",file_name,"_badsamples_at_cor",samp.filt.thresh,".txt",sep="")
bad_sample_names<-as.data.frame(bad_sample_names)
colnames(bad_sample_names)<-paste("Samples with correlation between technical replicates <", samp.filt.thresh,sep="")
write.table(bad_sample_names, file=feat.eval.outfile,sep="\t", row.names=FALSE)
}
bad_list={}
if(length(bad_samples)>0)
{
for(n1 in 1:length(bad_samples))
{
if(bad_samples[n1]>1)
{
bad_samples[n1]=bad_samples[n1]+(bad_samples[n1]-1)*(num_replicates-1)
}
}
for(n1 in 1:num_replicates)
{
bad_list<-c(bad_list,(bad_samples+n1-1))
}
bad_list<-bad_list[order(bad_list)]
}
if(i>1){
parent_bad_list<-intersect(parent_bad_list,bad_list)
}
else{
parent_bad_list<-bad_list
}
}
else
{
print("*********skipping feature evaluataion as only one replicate is present******")
}
}
cat("\n")
print("********Stage 2: Filtering results from each paramter setting based on sample and feature quality checks*********")
cat("\n")
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
feat.eval.file=paste(subdir2,"/",file_name,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtereddata.txt",sep="")
#data_rpd[[i]]=read.table(feat.eval.file,header=TRUE)
curdata<-data_rpd[[i]]
feat.eval.result.mat<-feateval_list[[i]]
if(length(parent_bad_list)>0){
curdata<-curdata[,-c(parent_bad_list+8)]
#maxint<-apply(curdata[,-c(1:4,((dim(curdata)[2]-6):dim(curdata)[2]))],1,max)
maxint<-apply(curdata[,-c(1:8)],1,max)
badfeats<-which(maxint==0)
if(length(badfeats)>0){
curdata<-curdata[-c(badfeats),]
feat.eval.result.mat<- feat.eval.result.mat[-c(badfeats),]
feateval_list[[i]]<-feat.eval.result.mat
}
}
data_rpd[[i]]<-curdata[which(as.numeric(feat.eval.result.mat$median)<=feat.filt.thresh),]
feat.eval.outfile=paste(subdir2,"/",file_name,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtereddata.txt",sep="")
#write results
write.table(data_rpd[[i]], feat.eval.outfile,sep="\t", row.names=FALSE)
feat.eval.outfile=paste(subdir1,"/",file_name,fileroot,"featureassessment.txt",sep="")
#write results
write.table(feateval_list[[i]], feat.eval.outfile,sep="\t", row.names=FALSE)
}
###########################################
#4) Merge two or more parameter settings
cat("\n")
print("*************Stage 3: merging features detected at different parameter settings********************")
cat("\n")
num_pairs=1
finalres={}
rnames={}
if(length(alignmentresults)>1){
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
bool_num<-1
for(j in i:length(alignmentresults))
{
bool_num<-1
#if(i!=j)
if(i==j){
if(length(alignmentresults)>1){
bool_num<-0
}
else{
bool_num<-1
}
}
#if(i!=j)
if(bool_num==1)
{
file_name=sapply(strsplit(alignmentresults[j],".txt"),head)
# if(i!=j)
{
p1_p2=paste("p",i,"_U_","p",j,sep="")
}
# else
#{
# p1_p2="p1"
#}
feat.eval.A<-feateval_list[[i]]
feat.eval.B<-feateval_list[[j]]
feat.eval.A<-feat.eval.A[which(as.numeric(feat.eval.A$median)<=feat.filt.thresh),]
feat.eval.B<-feat.eval.B[which(as.numeric(feat.eval.B$median)<=feat.filt.thresh),]
#print(p1_p2)
print(paste("Number of good quality features from setting ",i,":", dim(data_rpd[[i]])[1],sep=": "))
print(paste("Number of good quality features from setting ",j,":",dim(data_rpd[[j]])[1],sep=": "))
data_m=merge.Results(data_rpd[[i]],data_rpd[[j]],feat.eval.A,feat.eval.B,max.mz.diff,max.rt.diff, merge.eval.pvalue,alignment.tool="XCMS",
numnodes=numnodes, mult.test.cor,mergecorthresh,missingvalue)
numcols<-dim(data_m)[2]
data_m_int<-data_m[,-c(1:8,(numcols-7):numcols)]
numsamps<-dim(data_m_int)[2]/num_replicates
maxint<-apply(data_m_int,1,function(x){max(x,na.rm=TRUE)})
numpeaks<-lapply(1:dim(data_m_int)[1],function(j){length(which(is.na(data_m_int[j,])==FALSE))})
data_m[,8]<-numpeaks
if(is.na(minexp.pct)==FALSE)
{
minexp<-minexp.pct*dim(data_m_int)[2]
if(length(which(data_m[,8]>=minexp))>0){
data_m<-data_m[which(data_m[,8]>=minexp),]
}else{
stop(paste("No features have non-missing value in ",minexp, " samples",sep=""))
}
}
union_list[[num_pairs]]<-data_m[,c(1:8)]
#union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],numpeaks)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$numgoodsamples)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$median)
#Qscore<-(as.numeric(data_m$numgoodsamples)/as.numeric(data_m$median+0.1))
#Qscore<-100*(Qscore/numsamps)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$Qscore)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],maxint)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m_int)
featinfo<-colnames(data_m[,c(1:7)])
cnames<-colnames(data_m_int)
merge.res.colnames<-c(featinfo,"NumPres.All.Samples","NumPres.Biological.Samples",paste("median",fileroot,sep=""),"Qscore","Max.Intensity",cnames)
colnames(union_list[[num_pairs]])<-as.character(merge.res.colnames)
curres={}
curres=cbind(curres, p1_p2)
curres=cbind(curres, dim(union_list[[num_pairs]])[1])
curres=cbind(curres, mean(as.numeric(data_m$median)))
curres=cbind(curres, mean(as.numeric(data_m$Qscore)))
# curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$Qscore))))
curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$median))))
if(curscore>bestscore){
bestscore<-curscore
best_i<-num_pairs
best_pair<-p1_p2
}
curres=cbind(curres,curscore)
curres<-as.data.frame(curres)
finalres=rbind(finalres,curres)
finalname=paste("XCMS_feature_list_at_", p1_p2,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
#Output merge results
write.table(union_list[[num_pairs]],file=paste(subdir3,"/",finalname,sep=""), sep="\t",row.names=FALSE)
num_pairs=num_pairs+1
}
}
}
}else{
for(i in 1:length(alignmentresults))
{
file_name=sapply(strsplit(alignmentresults[i],".txt"),head)
j=i
{
if(i==j)
{
file_name=sapply(strsplit(alignmentresults[j],".txt"),head)
# if(i!=j)
{
p1_p2=paste("p",i,"_U_","p",j,sep="")
}
# else
#{
# p1_p2="p1"
#}
feat.eval.A<-feateval_list[[i]]
feat.eval.B<-feateval_list[[j]]
feat.eval.A<-feat.eval.A[which(as.numeric(feat.eval.A$median)<=feat.filt.thresh),]
feat.eval.B<-feat.eval.B[which(as.numeric(feat.eval.B$median)<=feat.filt.thresh),]
#print(p1_p2)
print(paste("Number of good quality features from setting ",i,":", dim(data_rpd[[i]])[1],sep=": "))
#print(paste("Number of good quality features from setting ",j,":",dim(data_rpd[[j]])[1],sep=": "))
data_m=merge.Results(data_rpd[[i]],data_rpd[[j]],feat.eval.A,feat.eval.B,max.mz.diff,max.rt.diff, merge.eval.pvalue,alignment.tool="XCMS",
numnodes=numnodes, mult.test.cor,mergecorthresh,missingvalue)
numcols<-dim(data_m)[2]
data_m_int<-data_m[,-c(1:8,(numcols-7):numcols)]
numsamps<-dim(data_m_int)[2]/num_replicates
maxint<-apply(data_m_int,1,function(x){max(x,na.rm=TRUE)})
numpeaks<-lapply(1:dim(data_m_int)[1],function(j){length(which(is.na(data_m_int[j,])==FALSE))})
data_m[,8]<-numpeaks
if(is.na(minexp.pct)==FALSE){
minexp<-minexp.pct*dim(data_m_int)[2]
if(length(which(data_m[,8]>=minexp))>0){
data_m<-data_m[which(data_m[,8]>=minexp),]
}else{
stop(paste("No features have non-missing value in ",minexp, " samples",sep=""))
}
}
union_list[[num_pairs]]<-data_m[,c(1:8)]
#union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],numpeaks)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$numgoodsamples)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$median)
#Qscore<-(as.numeric(data_m$numgoodsamples)/as.numeric(data_m$median+0.1))
#Qscore<-100*(Qscore/numsamps)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m$Qscore)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],maxint)
union_list[[num_pairs]]<-cbind(union_list[[num_pairs]],data_m_int)
featinfo<-colnames(data_m[,c(1:7)])
cnames<-colnames(data_m_int)
merge.res.colnames<-c(featinfo,"NumPres.All.Samples","NumPres.Biological.Samples",paste("median",fileroot,sep=""),"Qscore","Max.Intensity",cnames)
colnames(union_list[[num_pairs]])<-as.character(merge.res.colnames)
curres={}
curres=cbind(curres, p1_p2)
curres=cbind(curres, dim(union_list[[num_pairs]])[1])
curres=cbind(curres, mean(as.numeric(data_m$median)))
curres=cbind(curres, mean(as.numeric(data_m$Qscore)))
# curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$Qscore))))
curscore<-(dim(union_list[[num_pairs]])[1]-(scoreweight*mean(as.numeric(data_m$median))))
if(curscore>bestscore){
bestscore<-curscore
best_i<-num_pairs
best_pair<-p1_p2
}
curres=cbind(curres,curscore)
curres<-as.data.frame(curres)
finalres=rbind(finalres,curres)
finalname=paste("XCMS_feature_list_at_", p1_p2,"cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
#Output merge results
write.table(union_list[[num_pairs]],file=paste(subdir3,"/",finalname,sep=""), sep="\t",row.names=FALSE)
metlin.res={}
kegg.res={}
#length(union_list[[num_pairs]]$mz
num_pairs=num_pairs+1
}
}
}
}
finalres<-as.data.frame(finalres)
colnames(finalres)<-c("Parameter Combination", "Number of Features", "median PID/CV between sample replicates", "mean Qscore (Quality score)", "Parameter score")
write.table(finalres,file=paste(xMSanalyzer.outloc,"/Stage3a/xcms_with_xMSanalyzer_merge_summary.txt",sep=""), sep="\t", row.names=FALSE)
print("Most optimal feature setting:")
print(best_pair)
cat("\n")
#########################################################################
#rawQCeval/
cat("\n")
print(paste("********Stage 3b: Generating final (pre-batcheffect correction) untargeted and targeted feature tables using ",best_pair," results******",sep=""))
cat("\n")
# pdf("Stage4a_QC_plots.pdf")
#pdf(file=paste("subdir4a/Stage4a_QC_plots.pdf",sep=""))
pdf(file=paste(subdir4a,"/Stage4a_QC_plots.pdf",sep=""))
#most optimal set after merger
d1<-union_list[[best_i]]
if(is.na(refMZ)==FALSE){
stddata<-read.table(refMZ,sep="\t",header=TRUE)
print(refMZ)
print(head(stddata))
}else{
if(charge_type=="pos"){
data(example_target_list_pos)
stddata<-example_target_list_pos
}else{
if(charge_type=="neg"){
data(example_target_list_neg)
stddata<-example_target_list_neg
}else{
stop("Invalid option. \'charge_type\' should be \'pos\' or \'neg\'.")
}
}
}
Sys.sleep(1)
hist(d1$NumPres.All.Samples,main="Histogram NumPres.All.Samples",col="orange",xlab="Number of samples (including replicates) \n with non-zero intensity values", ylab="Number of features",cex.main=0.7)
hist(d1$NumPres.Biological.Samples,main="Histogram NumPres.Biological.Samples \n (using all data)",col="orange",xlab="Number of biological samples \n with non-zero intensity values", ylab="Number of features",cex.main=0.7)
#h1<-hist(d1$median_CV,main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
if(num_replicates>2){
h1<-hist(d1$median_CV,breaks=seq(0,max(d1$median_CV,na.rm=TRUE)+10,10),main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
}else{
h1<-hist(d1$median_PID,breaks=seq(0,max(d1$median_PID,na.rm=TRUE)+10,10),main="Histogram median CV \n (using all data)",col="orange",xlab="median CV%", ylab="Number of features",cex.main=0.7)
}
hist(d1$Qscore,main="Histogram Qscore \n (using all data)",col="orange",xlab="Quality score", ylab="Number of features",cex.main=0.7)
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=h1$breaks,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=h1$breaks,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
lab_text<-paste(h1$breaks,"-",h1$breaks+10,sep="")
#pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main="Pie chart of median CVs (%) \n using all features",cex.main=0.7)
if(num_replicates>2){
pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main=paste("Pie chart of median CVs (%) \n using all features\n; average=",mean(d1$median_CV),sep=""),cex.main=0.7)
}else{
pie(h1$counts,col=rainbow(length(h1$counts)),labels=lab_text,main=paste("Pie chart of median PIDs (%) \n using all features\n; average=",mean(d1$median_PID),sep=""),cex.main=0.7)
}
d2<-d1[order(d1$time),]
plot(d2$time,d2$Max.Intensity,main="TIC \n (using all data)",col="orange",xlab="Time (s)",ylab="Max intensity across all samples/profiles",cex.main=0.7)
plot(d2$time,d2$mz,main="m/z vs Time \n (using all data)",col="orange",xlab="Time (s)",ylab="m/z",cex.main=0.7)
plot(d2$time,d2$Qscore,main="Qscore vs Time \n (using all data)",col="orange",xlab="Time (s)",ylab="Qscore",cex.main=0.7)
plot(d2$mz,d2$Qscore,main="Qscore vs m/z \n (using all data)",col="orange",xlab="m/z",ylab="Qscore",cex.main=0.7)
max_numzeros<-dim(d1)[2]*1
if(is.na(void.vol.timethresh)==FALSE){
dfirst15<-d1[which(d1[,2]<void.vol.timethresh),]
if(nrow(dfirst15)>1){
print(dfirst15)
ind1<-which(dfirst15[,12]==max(dfirst15[,12]))
time_thresh<-dfirst15[ind1,2]
time_thresh<-time_thresh-(0.30*time_thresh)
# plot(dfirst15[,2],dfirst15[,9],xlab="Time (s)", ylab="Max intensity across all samples")
plot(dfirst15[,2],dfirst15[,9],xlab="Time (s)", ylab="Max intensity across all samples", main=paste("Estimated void volume time: ",time_thresh," s",sep=""))
abline(v=time_thresh,col=4,lty=3)
d1<-d1[which(d1$time>=time_thresh),]
print("Estimated void volume time cutoff")
print(time_thresh)
}
d1_int<-round(d1[,-c(1:12)],0)
d1<-cbind(d1[,c(1:12)],d1_int)
rm(d1_int)
#sfname<-paste(xMSanalyzer.outloc,"/stage5/feature_table_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,"filtered.txt",sep="")
finalname<-paste("featuretable_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,"_voidtimefilt.txt",sep="")
write.table(d1,file=paste(subdir3b,"/",finalname,sep=""),sep="\t",row.names=FALSE)
}else{
finalname<-paste("featuretable_",best_pair,"_cor",samp.filt.thresh,fileroot,feat.filt.thresh,".txt",sep="")
write.table(d1,file=paste(subdir3b,"/",finalname,sep=""),sep="\t",row.names=FALSE)
}
cat("\n")
print(paste("********Stage 4a: Performing QC checks using ",best_pair," results*******",sep=""))
cat("\n")
Sys.sleep(1)
print("Dim data after void time filtering")
print(dim(d1))
data_m<-d1[,-c(1:12)]
if(replacezeroswithNA==TRUE){
data_m<-replace(as.matrix(data_m),which(data_m==0),NA)
}
counna<-apply(data_m,1,function(x){length(which(is.na(x)==TRUE))})
maxzeros<-1*dim(data_m)[2]
if(length(which(counna<maxzeros))){
data_m<-data_m[which(counna<maxzeros),]
}
maxint<-apply(data_m,1,function(x){max(x,na.rm=TRUE)})
maxint_ord<-order(maxint,decreasing=TRUE)
#[maxint_ord[1:5000]
X<-t(data_m) #[maxint_ord[1:2000],])
X<-replace(as.matrix(X),which(is.na(X)==TRUE),0)
tic.eval(d1[,-c(1:12)],outloc=subdir4a)
feat.eval.result=evaluate.Features(d1[,-c(9:12)], numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
Sys.sleep(1)
s1<-"Stage 1 results: QC evaluation of invidual parameters from apLCMS"
s2<-"Stage 2 results: filtered results from each paramter setting based on sample and feature quality (CV within replicates) checks"
s3<-"Stage 3a results: merged results using stage 2 filtered files"
s3b<-"Stage 3b results: Final untargeted and targeted feature tables"
s4a<-"Stage 4a results: QC evaluation of targeted and untargeted data before batch-effect correction"
sm<-rbind(s1,s2,s3,s3b,s4a)
targeted_feat_raw<-eval.target.mz(dataA=d1[,-c(3:12)],refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=refMZ.mz.diff,timethresh=refMZ.time.diff,outloc=subdir4a,folderheader="raw")
if(is.na(sample_info_file)==FALSE)
{
#sampleid_mapping<-read.table(sample_info_file,sep="\t",header=TRUE)
sampleid_mapping[,1]<-gsub(sampleid_mapping[,1],pattern=filepattern,replacement="")
cnames<-colnames(data_m)
cnames<-gsub(cnames,pattern=filepattern,replacement="")
colnames(data_m)<-cnames
batch_inf<-sampleid_mapping[,3]
batch_labels<-as.factor(sampleid_mapping[,3])
l1<-levels(batch_labels)
pca.eval(X=X,samplelabels=batch_labels,filename="raw",ncomp=5,center=TRUE,scale=TRUE,legendlocation="topright",legendcex=0.5,outloc=subdir4a)
Sys.sleep(1)
if(dim(sampleid_mapping)[2]<4){
mod<-rep(1,dim(data_m)[2])
}else{
mod<-sampleid_mapping[,-c(1:3)]
}
dev.off()
Sys.sleep(1)
#######################################################################
cat("\n")
print("Stage 4b: Performing batch-effect correction and post-correction QC evaluation")
cat("\n")
# pdf("Stage4b_QC_plots.pdf")
#pdf(file=paste("subdir4b/Stage4b_QC_plots.pdf",sep=""))
pdf(file=paste(subdir4b,"/Stage4b_QC_plots.pdf",sep=""))
##################################################################
adjdata<-try(sva::ComBat(dat=data_m,batch=batch_inf,mod=mod,par.prior=TRUE),silent=TRUE)
if(is(adjdata,"try-error")){
data_m1<-cbind(d1[,c(1:4)],data_m)
#print(adjdata)
print("sva::ComBat caused an error.")
print(adjdata)
print("Too many missing values can cause this error. Trying MetabComBat...")
#adjdata<-MetabComBat(dat=data_m1,saminfo=sampleid_mapping,par.prior=T,filter=F,write=F)
adjdata<-MetabComBat(dat=data_m1,saminfo=sampleid_mapping,par.prior=T,filter=F,write=F,prior.plots=F)
adjdata<-adjdata[,-c(1:4)]
}
maxint<-apply(adjdata,1,function(x){max(x,na.rm=TRUE)})
adjdata2<-replace(as.matrix(adjdata),which(is.na(adjdata)==TRUE),0)
adjdata2<-replace(as.matrix(adjdata2),which(adjdata2<0),0)
adjdata2<-cbind(d1[,c(1:8)],adjdata2)
expression_xls<-paste(subdir4b,"/ComBat_corrected_",best_pair,"_feature_table.txt",sep="")
feat.eval.result=evaluate.Features(adjdata2, numreplicates=num_replicates,min.samp.percent=0.6,alignment.tool="XCMS",impute.bool=TRUE)
cnames=colnames(feat.eval.result)
feat.eval.result<-apply(feat.eval.result,2,as.numeric)
feat.eval.result<-as.data.frame(feat.eval.result)
feat.eval.result.mat=cbind(adjdata2[,c(1:4)],feat.eval.result)
numpeaks<-apply(adjdata2[,-c(1:8)],1,countpeaks)
maxint<-apply(adjdata2[,-c(1:8)],1,function(x){max(x,na.rm=TRUE)})
adjdata2<-cbind(adjdata2[,c(1:7)],numpeaks,feat.eval.result.mat$numgoodsamples,feat.eval.result.mat$median,feat.eval.result.mat$Qscore,maxint,adjdata2[,-c(1:8)])
colnames(adjdata2)<-colnames(d1)
write.table(adjdata2,file=paste(subdir4b,"/",expression_xls,sep=""),sep="\t",row.names=FALSE)
# X<-t(adjdata2[maxint_ord[1:2000],-c(1:9)])
tic.eval(adjdata2[,-c(1:12)],outloc=subdir4b)
X<-t(adjdata2[,-c(1:12)])
X<-as.matrix(X)
X<-replace(as.matrix(X),which(is.na(X)==TRUE),0)
pca.eval(X,samplelabels=batch_labels,filename="ComBat",ncomp=5,center=TRUE,scale=TRUE,legendlocation="topright",legendcex=0.5,outloc=subdir4b)
#eval.reference.metabs(dataA=adjdata2,stdData=stddata,mzthresh=10,outloc=getwd(),folderheader="ComBat")
#eval.target.mz(dataA=adjdata2,refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=10,outloc=subdir4b,folderheader="ComBat")
targeted_feat_combat<-eval.target.mz(dataA=adjdata2[,-c(3:12)],refMZ=stddata,feature.eval.result=feat.eval.result,mzthresh=refMZ.mz.diff,timethresh=refMZ.time.diff,outloc=subdir4b,folderheader="ComBat")
s4b<-"Stage 4b results: Batch-effect evaluation post ComBat and QC evaluation of targeted data post batch-effect correction"
sm<-rbind(sm,s4b)
}else{
adjdata2=NULL
targeted_feat_combat<-NULL
}
dev.off()
metlin.res={}
kegg.res={}
#length(union_list[[num_pairs]]$mz
if(is.na(adduct.list)==FALSE){
cat("\n")
print("*********Stage 5: Mapping m/z values to known metabolites using KEGG*********")
cat("\n")
annot.res<-feat.batch.annotation.KEGG(adjdata2,mz.tolerance.dbmatch,adduct.list,subdir5, numnodes=numnodes,syssleep=syssleep)
annotres_list<-annot.res
s5<-"Stage 5 results: Annotation of features"
sm<-rbind(sm,s5)
}
print("*************Processing complete**********")
#print("*********Characterizing metabolites*********")
}
else
{
stop(paste("No files exist in",XCMS.outloc, "Please check the input value for cdfloc", sep=""))
}
}
suppressWarnings(sink(file=NULL))
sm<-as.data.frame(sm)
colnames(sm)<-"Output_description"
setwd(xMSanalyzer.outloc)
write.table(sm,file="Readme.txt",sep="\t",row.names=FALSE)
print(paste("Processing is complete. Program results can be found at: ",xMSanalyzer.outloc,sep=""))
return(list("XCMS.merged.res"=union_list, "XCMS.ind.res"=data_rpd_all,"XCMS.ind.res.filtered"=data_rpd, "final.feat.table.annot"=annotres_list, "feat.eval.ind"=feateval_list, "sample.eval.ind"=rsqres_list,"final.feat.table.raw"=d1,"final.feat.table.combat"=adjdata2,
"final.targeted.feat.table.raw"=targeted_feat_raw,"final.targeted.feat.table.combat"=targeted_feat_combat))
}
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