--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/batchcorrection-57edfd3943ab/Normalisation_QCpool.r	Tue May 02 09:47:22 2017 -0400
@@ -0,0 +1,437 @@
+# Author: jfmartin
+# Modified by : mpetera
+###############################################################################
+# Correction of analytical effects inter and intra batch on intensities using quality control pooled samples (QC-pools)
+# according to the algorithm mentioned by Van der Kloet (J Prot Res 2009).
+# Parameters : a dataframe of Ions intensities and an other of samples? metadata which must contains at least the three following columns :
+#   "batch" to identify the batches of analyses ; need at least 3 QC-pools for linear adjustment and 8 for lo(w)ess adjustment
+#   "injectionOrder" integer defining the injection order of all samples : QC-pools and analysed samples
+#   "sampleType" indicates if defining a sample with "sample" or a QC-pool with "pool"
+# NO MISSING DATA are allowed
+# Version 0.91 insertion of ok_norm function to assess correction feasibility 
+# Version 0.92 insertion of slope test in ok_norm
+# Version 0.93 name of log file define as a parameter of the correction function
+# Version 0.94 Within a batch, test if all QCpools or samples values = 0. Definition of an error code in ok_norm function (see function for details)
+# Version 0.99 include non linear lowess correction. 
+# Version 1.00 the corrected result matrix is return transposed in Galaxy
+# Version 1.01 standard deviation=0 instead of sum of value=0 is used to assess constant data in ok_norm function. Negative values in corrected matrix are converted to 0. 
+# Version 1.02 plotsituation create a result file with the error code of non execution of correction set by function ok_norm
+# Version 1.03 fix bug in plot with "reg" option. suppression of ok_norm=4 condition if ok_norm function
+# Version 2.00 Addition of loess function, correction indicator, plots ; modification of returned objects' format, some plots' displays and ok_norm ifelse format
+# Version 2.01 Correction for pools negative values earlier in norm_QCpool
+# Version 2.10 Script refreshing ; vocabulary adjustment ; span in parameters for lo(w)ess regression ; conditionning for third line ACP display ; order in loess display
+# Version 2.11 ok1 and ok2 permutation (ok_norm) ; conditional display of regression (plotsituation) ; grouping of linked lignes + conditioning (normX) ; conditioning for CVplot
+# Version 2.20 acplight function added from previous toolBox.R [# Version 1.01 "NA"-coding possibility added in acplight function]
+# Version 2.30 addition of suppressWarnings() for known and controlled warnings ; suppression of one useless "cat" message ; change in Rdata names ; 'batch(es)' in cat
+# Version 2.90 change in handling of generated negative and Inf values
+# Version 2.91 Plot improvement
+
+ok_norm=function(qcp,qci,spl,spi,method) {
+  # Function used for one ion within one batch to determine whether or not batch correction is possible
+  # ok_norm values :
+  #   0 : no preliminary-condition problem
+  #   1 : standard deviation of QC-pools or samples = 0
+  #   2 : insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess)
+  #   3 : significant difference between QC-pools' and samples' means
+  #   4 : denominator =0 when on 1 pool per batch <> 0
+  #   5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2
+  
+  ok=0
+  if (method=="linear") {minQC=3} else {minQC=8}
+  if (length(qcp)<minQC) { ok=2 
+  } else {
+    if (sd(qcp)==0 | sd(spl)==0) { ok=1 
+    } else {
+    cvp= sd(qcp)/mean(qcp); cvs=sd(spl)/mean(spl)
+    rttest=t.test(qcp,y=spl)
+    reslsfit=lsfit(qci, qcp)
+    reslsfitSample=lsfit(spl, spi)
+    ordori=reslsfit$coefficients[1]
+    penteB=reslsfit$coefficients[2]
+    penteS=reslsfitSample$coefficients[2]
+    # Significant difference between samples and pools
+    if (rttest$p.value < 0.01) { ok=3 
+    } else {
+      # to avoid denominator =0 when on 1 pool per batch <> 0
+      if (method=="linear" & length(which(((penteB*qci)+ordori)==0))>0 ){ ok=6 
+      } else {
+      # different sloop between samples and pools
+      if (method=="linear" & penteB/penteS < -0.20) { ok=5 }
+  }}}}
+  ok_norm=ok
+}
+
+plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none") {
+    #	Check for all ions in every batch if linear or lo(w)ess correction is possible.
+    #	Use ok_norm function and create a file (PreNormSummary.txt) with the error code.
+    #	Also create a pdf file with plots of linear and lo(w)ess regression lines.
+    #	x: dataframe with ions in columns and samples in rows ; x is the result of concatenation of sample metadata file and ions file
+    #	nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType"
+    #	outfic: name of regression plots pdf file
+    #	fact: factor to be used as categorical variable for plots and PCA.  
+    indfact	=which(dimnames(x)[[2]]==fact)
+    indtypsamp	=which(dimnames(x)[[2]]=="sampleType") 
+    indbatch	=which(dimnames(x)[[2]]=="batch")
+    indinject	=which(dimnames(x)[[2]]=="injectionOrder")
+    lastIon=dim(x)[2]
+    nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns
+    nbb=length(levels(x$batch)) # Number of batch = number of levels of "batch" comlumn (factor)
+    nbs=length(x$sampleType[x$sampleType=="sample"])# Number of samples = number of rows with "sample" value in sampleType
+    pdf(outfic,width=27,height=7*ceiling((nbb+2)/3))
+    cat(nbi," ions ",nbb," batch(es) \n")
+    cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation de la dataset qui contiendra les CV
+    pre_bilan=matrix(0,nrow=nbi,ncol=3*nbb) # dataset of ok_norm function results	    
+    for (p in 1:nbi) {# for each ion
+        par (mfrow=c(ceiling((nbb+2)/3),3),ask=F,cex=1.2)
+        labion=dimnames(x)[[2]][p+nbid]
+        indpool=which(x$sampleType=="pool") # QCpools subscripts in x 
+        pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1)/mean(pools1)# CV before correction
+        for (b in 1:nbb) {# for each batch...
+            xb=data.frame(x[(x$batch==levels(x$batch)[b]),c(indtypsamp,indinject,p+nbid)])
+            indpb = which(xb$sampleType=="pool")# QCpools subscripts in the current batch
+            indsp = which(xb$sampleType=="sample")# samples subscripts in the current batch
+            indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool")# indices de tous les samples d'un batch pools+samples  
+            normLinearTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear")
+            normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
+            normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
+            #cat(dimnames(x)[[2]][p+nbid]," batch ",b," loess ",normLoessTest," linear ",normLinearTest,"\n")
+            pre_bilan[ p,3*b-2]=normLinearTest
+            pre_bilan[ p,3*b-1]=normLoessTest
+            pre_bilan[ p,3*b]=normLowessTest
+          if(length(indpb)>1){
+            if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span}
+            resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct") 
+            resloessSample=loess(xb[indsp,3]~xb[indsp,2],span=2*length(indpool)/nbs,degree=2,family="gaussian",iterations=4,surface="direct") 
+            reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)
+            reslowessSample=lowess(xb[indsp,2],xb[indsp,3])
+            liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
+            plot(xb[indsp,2],xb[indsp,3],pch=16, main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
+            points(xb[indpb,2], xb[indpb,3],pch=5)
+            points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")
+            points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)
+            points(reslowess,type="l",col="red"); points(reslowessSample,type="l",col="red",lty=2)
+            abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue")
+            abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")
+            legend("topleft",c("pools","samples"),lty=c(1,2),bty="n")
+            legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n")
+          }
+        }
+# series de plot avant et apres correction
+minval=min(x[p+nbid]);maxval=max(x[p+nbid])
+plot( x$injectionOrder, x[,p+nbid],col=x$batch,ylim=c(minval,maxval),ylab=labion,
+      main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"))
+suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
+    }
+dev.off()
+pre_bilan=data.frame(pre_bilan)
+labion=dimnames(x)[[2]][nbid+1:nbi]
+for (i in 1:nbb) {
+  dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear")
+  dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess")
+  dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess")
+}
+bilan=data.frame(labion,pre_bilan)
+write.table(bilan,file=outres,sep="\t",row.names=F,quote=F)
+}
+
+
+normlowess=function (xb,detail="no",vref=1,b,span=NULL) {
+  #	Correction function applied to 1 ion in 1 batch. Use a lowess regression computed on QC-pools in order to correct samples intensity values
+  #	xb : dataframe for 1 ion in columns and samples in rows.
+  # vref : reference value (average of ion)
+  # b : batch subscript
+  # nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType"
+  indpb = which(xb$sampleType=="pool") # pools subscripts of current batch
+  indsp = which(xb$sampleType=="sample") # samples of current batch subscripts
+  indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool");# batch subscripts of all samples and QC-pools
+  labion=dimnames(xb)[[2]][3]
+  newval=xb[[3]] # initialisation of corrected values = intial values
+  ind <- 0 # initialisation of correction indicator
+  normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
+  #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
+  if (normTodo==0) {
+    if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span}
+    reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools 
+    px=xb[indsp,2];  # vector of injectionOrder values only for samples  
+    for(j in 1:length(indbt)) {	 
+      if (xb$sampleType[j]=="pool") {
+        if (reslowess$y[which(indpb==j)]==0) reslowess$y[which(indpb==j)] <- 1
+        newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)])} 
+      else { # for samples, the correction value cor correspond to the nearest QCpools 
+        cor= reslowess$y[which(abs(reslowess$x-px[which(indsp==j)])==min(abs(reslowess$x - px[which(indsp==j)])))]
+        if (length(cor)>1) {cor=cor[1]}
+        if (cor <= 0) {cor=vref} # no modification of initial value
+        newval[j]=(vref*xb[j,3]) / cor
+      }
+    }
+    if (detail=="reg") {
+      liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
+      plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
+      points(xb[indpb,2], xb[indpb,3],pch=5)
+      points(reslowess,type="l",col="red")
+    }
+    ind <- 1
+  } else {# if ok_norm <> 0 , we perform a correction based on batch samples average
+    moySample=mean(xb[indsp,3]);if (moySample==0) moySample=1
+    newval[indsp] = (vref*xb[indsp,3])/moySample
+    if(length(indpb)>0){
+      moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
+      newval[indpb] = (vref*xb[indpb,3])/moypool
+    }
+  }
+  newval <- list(norm.ion=newval,norm.ind=ind)
+  return(newval)
+}
+
+normlinear <- function (xb,detail="no",vref=1,b,valneg=0) {
+  # Correction function applied to 1 ion in 1 batch.
+  # Use a linear regression computed on QC-pools in order to correct samples intensity values
+  # xb: dataframe with ions in columns and samples in rows; x is a result of concatenation of sample metadata file and ion file 
+  # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder" and "sampleType"
+  # b: which batch it is
+  # valneg: to determine what to do with generated negative and Inf values
+  indpb = which(xb$sampleType=="pool")# pools subscripts of current batch
+  indsp = which(xb$sampleType=="sample")# samples of current batch subscripts
+  indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool") # QCpools and samples of current batch subscripts
+  labion=dimnames(xb)[[2]][3]
+  newval=xb[[3]] # initialisation of corrected values = intial values	
+  ind <- 0 # initialisation of correction indicator
+  normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear")
+  if (normTodo==0) {
+    ind <- 1
+    reslsfit=lsfit(xb[indpb,2],xb[indpb,3])       # linear regression for QCpools 
+    reslsfitSample=lsfit(xb[indsp,2],xb[indsp,3]) # linear regression for samples
+    ordori=reslsfit$coefficients[1]
+    pente=reslsfit$coefficients[2]
+    if (detail=="reg") {
+      liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
+      plot(xb[indsp,2],xb[indsp,3],pch=16,
+           main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
+      points(xb[indpb,2], xb[indpb,3],pch=5)
+      abline(reslsfit)
+      abline(reslsfitSample,lty=2)
+    }
+    # correction with rescaling of ion global intensity (vref)
+    newval = (vref*xb[indbt,3]) / (pente * (xb[indbt,2]) + ordori)
+	newval[which((pente * (xb[indbt,2]) + ordori)<1)] <- -1 # to handle cases where 0<denominator<1
+	# handling if any negative values (or null denominators)
+	if(length(which((newval==Inf)|(newval<0)))!=0){
+	  toajust <- which((newval==Inf)|(newval<0))
+	  if(valneg=="NA"){
+	    newval[toajust] <- NA
+	  } else {
+	    newval[toajust] <- vref * (xb[indbt,3][toajust]) / mean(xb[indbt,3])
+    ### Other possibility
+	##  if(pente>0){ # slope orientation
+	##    newval[toajust]<-(vref*(xb[indbt,3][toajust]))/(pente*ceiling(-ordori/pente+1.00001)+ordori)
+	##  }else{
+	##    newval[toajust]<-(vref*(xb[indbt,3][toajust]))/(pente*floor(-ordori/pente-1.00001)+ordori)
+	##  }
+	  }
+	}
+  } else {# if ok_norm!=0 , we perform a correction based on batch samples average.
+    moySample=mean(xb[indsp,3]); if (moySample==0) moySample=1
+    newval[indsp] = (vref*xb[indsp,3])/moySample
+    if(length(indpb)>0){
+      moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
+      newval[indpb] = (vref*xb[indpb,3])/moypool
+    }
+  }
+  newval <- list(norm.ion=newval,norm.ind=ind)
+  return(newval)
+}
+
+
+normloess <- function (xb,detail="no",vref=1,b,span=NULL) {
+    #	Correction function applied to 1 ion in 1 batch. 
+    #   Use a loess regression computed on QC-pools in order to correct samples intensity values.
+    #	xb : dataframe for 1 ion in columns and samples in rows.
+    #   detail : level of detail in the outlog file.
+    #   vref : reference value (average of ion)
+    #   b : batch subscript
+    indpb = which(xb$sampleType=="pool") # pools subscripts of current batch
+    indsp = which(xb$sampleType=="sample") # samples of current batch subscripts
+    indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool");# batch subscripts of all samples and QCpools
+    labion=dimnames(xb)[[2]][3]
+    newval=xb[[3]] # initialisation of corrected values = intial values
+    ind <- 0 # initialisation of correction indicator
+    normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
+    #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
+    if (normTodo==0) { 
+        if(length(span)==0){span1<-1}else{span1<-span}
+        resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct") # loess regression with QCpools 
+        cor=predict(resloess,newdata=xb[,2])
+		    cor[cor<=1] <- 1 
+        newval=(vref*xb[,3]) / cor 
+        if(length(which(newval>3*(quantile(newval)[4])))>0){ # in this case no modification of initial value
+			newval <- xb[,3]} else {ind <- 1} # confirmation of correction
+        if ((detail=="reg")&(ind==1)) { # plot
+            liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
+            plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
+            points(xb[indpb,2], xb[indpb,3],pch=5)
+            points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="red")
+        }
+    } 
+    if (ind==0) {# if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch samples average
+      moySample=mean(xb[indsp,3]);if (moySample==0) moySample=1
+      newval[indsp] = (vref*xb[indsp,3])/moySample
+      if(length(indpb)>0){
+        moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
+        newval[indpb] = (vref*xb[indpb,3])/moypool
+      }
+    }
+    newval <- list(norm.ion=newval,norm.ind=ind)
+    return(newval)
+}
+
+
+
+norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=F, NormInt=F, method="linear",span="none",valNull="0")
+{
+  ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe.
+  # x: dataframe with ions in column and samples' metadata
+  # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType"
+  # outlog: name of regression plots and PCA pdf file
+  # fact: factor to be used as categorical variable for plots  
+  # metaion: dataframe of ions' metadata
+  # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction.
+  #          detail="plot" with plot for all batch before and after correction.
+  #          detail="reg" with added plots with regression lines for all batches.
+  # NormMoyPool: not used
+  # NormInt: not used
+  # method: regression method to be used to correct : "linear" or "lowess" or "loess"
+  # valNull: to determine what to do with negatively estimated intensities
+	indfact		=which(dimnames(x)[[2]]==fact)
+	indtypsamp=which(dimnames(x)[[2]]=="sampleType")
+	indbatch	=which(dimnames(x)[[2]]=="batch")
+	indinject	=which(dimnames(x)[[2]]=="injectionOrder")
+	lastIon=dim(x)[2]
+	valref=apply(as.matrix(x[,(nbid+1):(lastIon)]),2,mean) # reference value for each ion used to still have the same rought size of values
+	nbi=lastIon-nbid # number of ions
+	nbb=length(levels(x$batch)) # Number of batch(es) = number of levels of factor "batch" (can be =1)
+	nbs=length(x$sampleType[x$sampleType=="sample"])# Number of samples 
+	nbp=length(x$sampleType[x$sampleType=="pool"])# Number of QCpools
+	Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nbp+nbs,ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe)
+  dimnames(Xn)=dimnames(x)
+	cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction
+	dimnames(cv)[[2]]=c("avant","apres")
+	if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"}
+	pdf(outlog,width=27,height=20)
+	cat(nbi," ions ",nbb," batch(es) \n")
+	if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
+  res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep="")))
+	for (p in 1:nbi) {# for each ion
+	  labion=dimnames(x)[[2]][p+nbid]
+        if (detail == "reg") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
+		indpool=which(x$sampleType=="pool")# QCpools subscripts in all batches
+		pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1)/mean(pools1)# CV before correction
+		for (b in 1:nbb) {# for every batch
+			indpb = which(x$batch==levels(x$batch)[b] & x$sampleType=="pool")# QCpools subscripts of the current batch	
+			indsp = which(x$batch==levels(x$batch)[b] & x$sampleType=="sample")# samples subscripts of the current batch
+      indbt = which(x$batch==levels(x$batch)[b] & (x$sampleType=="pool" | x$sampleType=="sample")) # subscripts of all samples
+      # cat(dimnames(x)[[2]][p+nbid]," indsp:",length(indsp)," indpb=",length(indpb)," indbt=",length(indbt)," ")
+			sub=data.frame(x[(x$batch==levels(x$batch)[b]),c(indtypsamp,indinject,p+nbid)])
+			if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull)
+			} else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span)
+			  } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span)
+          } else {stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n")}
+			}}
+			Xn[indbt,p+nbid] = res.norm[[1]]
+			res.ind[p,b] <- res.norm[[2]]
+			# CV batch test : if after normaliszation, CV before < CV after initial values are kept
+#  			moypoolRaw=mean(x[indpb,p+nbid])  ; if (moypoolRaw==0) moypoolRaw=1
+#			moySampleRaw=mean(x[indsp,p+nbid]); if (moySampleRaw==0) moySampleRaw=1
+#			moypool=mean(Xn[indpb,p+nbid]) ; if (moypool==0) moypool=1
+#			#moySample=mean(Xn[indsp,p+nbid]); if (moySample==0) moySample=1
+#			if (sd( Xn[indpb,p+nbid])/moypool>sd(x[indpb,p+nbid])/moypoolRaw) { 
+#					Xn[indpb,p+nbid] = (valref[p]*x[indpb,p+nbid])/moypoolRaw
+#					Xn[indsp,p+nbid] = (valref[p]*x[indsp,p+nbid])/moySampleRaw
+#			}
+		}	
+		Xn[indpool,p+nbid][Xn[indpool,p+nbid]<0] <- 0
+		pools2=Xn[indpool,p+nbid]; cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE)# CV apres correction
+		if (detail=="reg" || detail=="plot" ) {
+		  	# plot before and after correction
+		  	minval=min(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE);maxval=max(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE)
+		  	plot( x$injectionOrder, x[,p+nbid],col=x$batch,ylab=labion,ylim=c(minval,maxval),
+              main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"))
+              points(x$injectionOrder[indpool],x[indpool,p+nbid],col="maroon",pch=".",cex=2)
+		  	plot(Xn$injectionOrder,Xn[,p+nbid],col=x$batch,ylab="",ylim=c(minval,maxval),
+             main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"))
+              points(Xn$injectionOrder[indpool],Xn[indpool,p+nbid],col="maroon",pch=".",cex=2)
+		  	suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
+		  	suppressWarnings(plot.design(Xn[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect after correction"))
+		}
+	}
+  ### Replacement of post correction negative values by chosen value
+	Xnn=Xn
+	for (i in c((nbid+1):dim(Xn)[2])) {
+	  cneg=which(Xn[[i]]<0)
+	  Xnn[[i]]=replace(Xn[[i]],cneg,as.numeric(valNull))
+	}
+  Xn=Xnn
+
+	if (detail=="reg" || detail=="plot" || detail=="no") {
+		if (nbi > 3) {
+			par(mfrow=c(3,4),ask=F,cex=1.2) # PCA Plot before/after, normed only and ions plot
+			acplight(x[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE)
+			norm.ion <- which(colnames(Xn)%in%(rownames(res.ind)[which(rowSums(res.ind)>=1)]))
+			acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE,norm.ion)
+			if(length(norm.ion)>0){acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,norm.ion)],"uv",TRUE)}
+			par(mfrow=c(1,2),ask=F,cex=1.2) # Before/after boxplot
+			cvplot=cv[!is.na(cv[[1]])&!is.na(cv[[2]]),]
+      if(nrow(cvplot)>0){
+			  boxplot(cvplot[[1]],ylim=c(min(cvplot),max(cvplot)),main="CV before correction")
+        boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV after correction")
+      }
+      dev.off()
+		}
+	}
+  if (nbi<=3) {dev.off()}
+  # transposed matrix is return  (format of the initial matrix with ions in rows)
+  Xr=Xn[,-c(1:nbid)]; dimnames(Xr)[[1]]=Xn[[1]]
+  Xr=t(Xr) ; Xr <- data.frame(ions=rownames(Xr),Xr)
+  
+  res.norm[[1]] <- Xr ; res.norm[[2]] <- data.frame(metaion,res.ind) ; res.norm[[3]] <- x[,c(1:nbid)]
+  names(res.norm) <- c("dataMatrix","variableMetadata","sampleMetadata")
+  return(res.norm)
+}
+
+
+
+
+
+acplight <- function(ids, scaling="uv", indiv=FALSE,indcol=NULL) {
+	suppressPackageStartupMessages(library(ade4))
+	suppressPackageStartupMessages(library(pcaMethods))
+  # Make a PCA and plot scores and loadings.
+  # First column must contain samples' identifiers.
+  # Columns 2 to 4 contain factors to colour the plots. 
+  for (i in 1:3) {
+    idss=ids[which(ids[,i+1]!="NA"),]
+    idss=data.frame(idss[idss[,i+1]!="",])
+    classe=as.factor(idss[[i+1]])
+    idsample=as.character(idss[[1]])
+    colour=1:length(levels(classe))
+    ions=as.matrix(idss[,5:dim(idss)[2]])
+	# Removing ions containing NA (not compatible with standard PCA)
+	ions=t(na.omit(t(ions)))
+	if(i==1){if(ncol(ions)!=(ncol(idss)-4)){cat("Note:",(ncol(idss)-4)-ncol(ions),"ions were ignored for PCA display due to NA in intensities.\n")}}
+    # Scaling choice: "uv","none","pareto"
+    object=suppressWarnings(prep(ions, scale=scaling, center=TRUE))
+	if(i==1){if(length(which(apply(ions,2,var)==0))>0){cat("Warning: there are",length(which(apply(ions,2,var)==0)),"constant ions.\n")}}
+    # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F
+    result <- pca(object, center=F, method="svd", nPcs=2)
+    # ADE4 : to plot samples' ellipsoid for each class
+    s.class(result@scores, classe, cpoint = 1,xax=1,yax=2,col=colour,sub=sprintf("Scores - PCs %sx%s",1,2), possub="bottomright")
+    #s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright")
+    if(i==1){resulti <- result}
+  }
+  if(indiv) {
+    colour <- rep("darkblue",length(resulti@loadings)) ; if(!is.null(indcol)) {colour[-c(indcol)] <- "red"}
+    plot(resulti@loadings,col=colour,main="Loadings",xaxt="n",yaxt="n",pch=20,
+         xlab=bquote(PC1-R^2==.(resulti@R2[1])),ylab=bquote(PC2 - R^2 == .(resulti@R2[2])))
+    abline(h=0,v=0)}
+}
+
+