diff BC/batch_correction_3Lfct.R @ 4:23314e1192d4 draft default tip

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/BC/batch_correction_3Lfct.R	Thu Jan 14 09:56:58 2021 +0000
@@ -0,0 +1,541 @@
+# 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
+# Version 3.00 - handling of sample tags' parameters
+#              - accepting sample types beyond "pool" and "sample"
+#              - dealing with NA
+#              - changes in the normalisation strategy regarding mean values to adjust for NA or 0 values
+#              - changes in the normalisation strategy regarding unconsistant values (negative or Inf)
+
+ok_norm=function(qcp,qci,spl,spi,method,normref=NA,valimp="0") {
+  # 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)
+  #   2.5 : less than 2 samples within a batch
+  #   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
+  #   6 : (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA
+  # Parameters:
+  # qcp: intensity of a given ion for pools
+  # qci: injection numbers for pools
+  # spl: intensity of a given ion for samples
+  # spi: injection numbers for samples
+  # method: to provide specific checks for "linear"
+  
+  ok=0
+  if (method=="linear") {minQC=3} else {minQC=8}
+  if (length(qcp[!is.na(qcp)])<minQC) { ok=2 } else { if (length(spl[!is.na(spl)])<2) { ok=2.5
+  } else { 
+    if (sd(qcp,na.rm=TRUE)==0 | sd(spl,na.rm=TRUE)==0) { ok=1 
+    } else {
+    cvp= sd(qcp,na.rm=TRUE)/mean(qcp,na.rm=TRUE); cvs=sd(spl,na.rm=TRUE)/mean(spl,na.rm=TRUE)
+    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 
+        } else {
+          # 
+          if (method=="linear" & !is.na(normref) & valimp=="NA") {
+            denom = (penteB * c(spi,qci) + ordori)
+            normval = c(spl,qcp)*normref / denom
+            if(length(which((normval==Inf)|(denom<1)))==length(normval)){ok=6}
+          } 
+  }}}}}} 
+  ok_norm=ok
+}
+
+plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none",
+                           sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", 
+                           sampleTag=list(pool="pool",blank="blank",sample="sample"))) {
+    # Checks for all ions in every batch if linear or lo(w)ess correction is possible.
+    # Uses ok_norm function and creates a file (PreNormSummary.txt) with the corresponding error codes.
+    # Also creates a pdf file with plots of linear and lo(w)ess regression lines.
+    # Parameters:
+    # 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
+    # outres: name of summary table file
+    # fact: factor to be used as categorical variable for plots and PCA
+    # span: span value for lo(w)ess regression; "none" for linear or default values
+    # sm_meta: list of information about sample metadata coding
+    indfact=which(dimnames(x)[[2]]==fact)
+    indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType) 
+    indbatch=which(dimnames(x)[[2]]==sm_meta$batch)
+    indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder)
+    lastIon=dim(x)[2]
+    nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns
+    nbb=length(levels(x[[sm_meta$batch]])) # Number of batch = number of levels of "batch" comlumn (factor)
+    nbs=length(x[[sm_meta$sampleType]][x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$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[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in x 
+        pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction
+        for (b in 1:nbb) {# for each batch...
+            xb=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)])
+            indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in the current batch
+            indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples subscripts in the current batch
+            normLinearTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=mean(xb[c(indpb,indsp),3],na.rm=TRUE),valimp="NA")
+            normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
+            normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
+            pre_bilan[ p,3*b-2]=normLinearTest
+            pre_bilan[ p,3*b-1]=normLoessTest
+            pre_bilan[ p,3*b]=normLowessTest
+          if(length(indpb)>1){ 
+            if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span}
+            if(normLoessTest!=2){resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct")}
+            if(length(which(!(is.na(xb[indsp,3]))))>1){resloessSample=loess(xb[indsp,3]~xb[indsp,2],span=2*length(indpool)/nbs,degree=2,family="gaussian",iterations=4,surface="direct") }
+            if(normLowessTest!=2){reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)}
+            if(length(which(!(is.na(xb[indsp,3]))))>1){reslowessSample=lowess(xb[indsp,2],xb[indsp,3])}
+            liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
+            firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
+            plot(xb[indsp,2],xb[indsp,3],pch=16, main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj))
+            if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18,col="grey")}
+            points(xb[indpb,2], xb[indpb,3],pch=5)
+            if(normLoessTest!=2){points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")}
+            if(length(which(!(is.na(xb[indsp,3]))))>1){points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)}
+            if(normLowessTest!=2){points(reslowess,type="l",col="red")}; if(length(which(!(is.na(xb[indsp,3]))))>1){points(reslowessSample,type="l",col="red",lty=2)}
+            abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue")
+            if(length(which(!(is.na(xb[indsp,3]))))>1){abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")}
+            legend("topleft",c("pools","samples"),lty=c(1,2),bty="n")
+            legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n")
+          } else {
+            plot.new()
+            legend("center","Plot only available when the\nbatch contains at least 2 pools.")
+          }
+        }
+        # series de plot avant correction
+        minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE)
+        plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylim=c(minval,maxval),ylab=labion,
+              main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order")
+        suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
+    }
+    dev.off()
+    pre_bilan=data.frame(pre_bilan)
+    labion=dimnames(x)[[2]][nbid+1:nbi]
+    for (i in 1:nbb) {
+      dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear")
+      dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess")
+      dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess")
+    }
+    bilan=data.frame(labion,pre_bilan)
+    write.table(bilan,file=outres,sep="\t",row.names=F,quote=F)
+}
+
+
+normlowess=function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", 
+                     sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
+  # Correction function applied to 1 ion in 1 batch. 
+  # Uses a lowess regression computed on QC-pools in order to correct samples intensity values
+  # xb: dataframe for 1 ion in columns and samples in rows.
+  # vref: reference value (average of ion)
+  # b: batch subscript
+  # detail: level of detail in the outlog file
+  # span: span value for lo(w)ess regression; NULL for default values
+  # valneg: to determine what to do with generated negative and Inf values
+  # sm_meta: list of information about sample metadata coding
+  # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
+  indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch
+  indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts
+  labion=dimnames(xb)[[2]][3]
+  newval=xb[[3]] # initialisation of corrected values = intial values
+  ind <- 0 # initialisation of correction indicator
+  normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
+  #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
+  if (normTodo==0) {
+    if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span}
+    reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools 
+    if(length(which(reslowess$y<min_norm))!=0){ # to handle cases where 0<denominator<min_norm or negative
+      toajust <- which(reslowess$y<min_norm)
+      if(valneg=="NA"){ reslowess$y[toajust] <- NA
+      } else { if(valneg=="0"){ reslowess$y[toajust] <- -1
+        } else {
+          mindenom <- min(reslowess$y[reslowess$y>=min_norm],na.rm=TRUE)
+          reslowess$y[toajust] <- mindenom
+    } } }
+    for(j in 1:nrow(xb)) {
+      if (j %in% indpb) {
+        newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)])
+      } else { # for samples other than pools, the correction value "corv" correspond to the nearest QCpools 
+        corv= reslowess$y[which(abs(reslowess$x-xb[j,2])==min(abs(reslowess$x-xb[j,2]),na.rm=TRUE))]
+        if (length(corv)>1) {corv=corv[1]}
+        newval[j]=(vref*xb[j,3]) / corv
+      }
+      if((!is.na(newval[j]))&(newval[j]<0)){newval[j]<-0}
+    }
+    if (detail=="reg") {
+      liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
+      firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
+      plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj))
+      if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
+      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 pool or sample average
+    if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
+      moypool=mean(xb[indpb,3],na.rm=TRUE)
+      newval = (vref*xb[,3])/moypool
+    } else { 
+      moysamp=mean(xb[indsp,3],na.rm=TRUE)
+      if((!is.na(moysamp))&(moysamp>0)){
+        cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
+        newval = (vref*xb[,3])/moysamp
+      } else {
+        dev.off()
+        stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
+      }
+    }
+  }
+  newval <- list(norm.ion=newval,norm.ind=ind)
+  return(newval)
+}
+
+normlinear <- function (xb,detail="no",vref=1,b,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", 
+                        sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
+  # Correction function applied to 1 ion in 1 batch.
+  # Uses 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 
+  # detail: level of detail in the outlog file
+  # vref: reference value (average of ion)
+  # b: which batch it is
+  # valneg: to determine what to do with generated negative and Inf values
+  # sm_meta: list of information about sample metadata coding
+  # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
+  indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# pools subscripts of current batch
+  indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# 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",normref=vref,valimp=valneg)
+  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[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
+      firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
+      plot(xb[indsp,2],xb[indsp,3],pch=16,
+           main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj))
+      if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
+      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[,3]) / (pente * (xb[,2]) + ordori)
+    newval[which((pente * (xb[,2]) + ordori)<min_norm)] <- -1 # to handle cases where 0<denominator<1 or negative
+    # handling if any negative values
+    if(length(which((newval==Inf)|(newval<0)))!=0){
+      toajust <- which((newval==Inf)|(newval<0))
+      if(valneg=="NA"){ newval[toajust] <- NA
+      } else { if(valneg=="0"){ newval[toajust] <- 0
+        } else {
+          mindenom <- (pente * (xb[,2]) + ordori)
+          mindenom <- min(mindenom[mindenom>=min_norm],na.rm=TRUE)
+          newval[toajust] <- vref * (xb[,3][toajust]) / mindenom
+        }
+      }
+    }
+  } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average
+    if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
+      moypool=mean(xb[indpb,3],na.rm=TRUE)
+      newval = (vref*xb[,3])/moypool
+    } else { 
+      moysamp=mean(xb[indsp,3],na.rm=TRUE)
+      if((!is.na(moysamp))&(moysamp>0)){
+        cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
+        newval = (vref*xb[,3])/moysamp
+      } else {
+        dev.off()
+        stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
+      }
+    }
+  }
+  newval <- list(norm.ion=newval,norm.ind=ind)
+  return(newval)
+}
+
+
+normloess <- function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", 
+                       sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
+    # Correction function applied to 1 ion in 1 batch. 
+    # Uses 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
+    # span: span value for lo(w)ess regression; NULL for default values
+    # valneg: to determine what to do with generated negative and Inf values
+    # sm_meta: list of information about sample metadata coding
+    # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
+    indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch
+    indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts
+    indbt = which(xb[[sm_meta$sampleType]] %in% c(sm_meta$sampleTag$sample,sm_meta$sampleTag$pool))# batch subscripts of 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")
+    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 
+        corv=predict(resloess,newdata=xb[,2])
+        if(length(which(corv<min_norm))!=0){ # unconsistant values handling
+          toajust <- which(corv<min_norm)
+          if(valneg=="NA"){ corv[toajust] <- NA
+          } else { if(valneg=="0"){ corv[toajust] <- -1
+            } else {
+              mindenom <- min(corv[corv>=min_norm],na.rm=TRUE)
+              corv[toajust] <- mindenom
+            }
+          }
+        } 
+        newvalps=(vref*xb[indbt,3]) / corv[indbt] # to check if correction generates outlier values
+        refthresh=max(c(3*(quantile(newvalps,na.rm=TRUE)[4]),1.3*(xb[indbt,3])),na.rm=TRUE)
+        if(length(which(newvalps>refthresh))>0){ # if outliers
+          # in this case no modification of initial value
+          newval <- xb[,3]
+        } else { 
+          newval=(vref*xb[,3]) / corv 
+          newval[newval<0] <- 0
+          ind <- 1 # confirmation of correction 
+        }
+        if ((detail=="reg")&(ind==1)) { # plot
+            liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
+            firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
+            plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj))
+            if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
+            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 pool or sample average
+      if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
+        moypool=mean(xb[indpb,3],na.rm=TRUE)
+        newval = (vref*xb[,3])/moypool
+      } else { 
+        moysamp=mean(xb[indsp,3],na.rm=TRUE)
+        if((!is.na(moysamp))&(moysamp>0)){
+          cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
+          newval = (vref*xb[,3])/moysamp
+        } else {
+          dev.off()
+          stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
+        }
+      }
+    }
+    newval <- list(norm.ion=newval,norm.ind=ind)
+    return(newval)
+}
+
+
+
+norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=FALSE, NormInt=FALSE, method="linear",span="none",valNull="0",
+                         sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", 
+                         sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1) {
+  ### 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"
+  # span: span value for lo(w)ess regression; "none" for linear or default values
+  # valNull: to determine what to do with negatively estimated intensities
+  # sm_meta: list of information about sample metadata coding
+  # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
+    indfact=which(dimnames(x)[[2]]==fact)
+    indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType)
+    indbatch=which(dimnames(x)[[2]]==sm_meta$batch)
+    indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder)
+    lastIon=dim(x)[2]
+    indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in all batches
+    valref=apply(as.matrix(x[indpool,(nbid+1):(lastIon)]),2,mean,na.rm=TRUE) # 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[[sm_meta$batch]])) # Number of batch(es) = number of levels of factor "batch" (can be =1)
+    Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nrow(x),ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe)
+    dimnames(Xn)=dimnames(x)
+    cv=data.frame(matrix(NA,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction
+    dimnames(cv)[[2]]=c("avant","apres")
+    if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"}
+    pdf(outlog,width=27,height=20)
+    cat(nbi," ions ",nbb," batch(es) \n")
+    if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
+    res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep="")))
+    for (p in 1:nbi) {# for each ion
+      labion=dimnames(x)[[2]][p+nbid]
+      pools1=x[indpool,p+nbid]
+      if(length(which(pools1[!(is.na(pools1))]>0))<2){ # if not enough pools >0 -> no normalisation
+        war.note <- paste("Warning: less than 2 pools with values >0 in",labion,"-> no normalisation for this ion.")
+        cat(war.note,"\n")
+        Xn[,p+nbid] <- x[,p+nbid]
+        res.ind[p,] <- rep(0,nbb)
+        if (detail=="reg" || detail=="plot" ) {
+          par(mfrow=c(2,2),ask=F,cex=1.5)
+          plot.new()
+          legend("center",war.note)
+          minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE)
+          plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval),
+          main="No correction",xlab="injection order")
+          points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1)
+        }
+      } else {
+        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)}}
+        if (detail == "plot") {par(mfrow=c(2,2),ask=F,cex=1.5)}
+        cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction
+        for (b in 1:nbb) {# for every batch
+            indbt = which(x[[sm_meta$batch]]==(levels(x[[sm_meta$batch]])[b])) # subscripts of all samples
+            sub=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)])
+            if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull,sm_meta,min_norm)
+            } else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm)
+              } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm)
+                } 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]]
+        }
+        # Post correction CV calculation
+        pools2=Xn[indpool,p+nbid]
+        cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE)
+        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[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval),
+              main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order")
+              points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1)
+            plot(Xn[[sm_meta$injectionOrder]],Xn[,p+nbid],col=x[[sm_meta$batch]],ylab="",ylim=c(minval,maxval),
+             main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"),xlab="injection order")
+              points(Xn[[sm_meta$injectionOrder]][indpool],Xn[indpool,p+nbid],col="maroon",pch=16,cex=1)
+            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"))
+        }
+      }
+    }
+
+    if (detail=="reg" || detail=="plot" || detail=="no") {
+        if (nbi > 3) {
+            # Sum of ions before/after plot
+            par(mfrow=c(1,2),ask=F,cex=1.2)
+            xsum <- rowSums(x[,(nbid+1):lastIon],na.rm=TRUE)
+            Xnsum <- rowSums(Xn[,(nbid+1):lastIon],na.rm=TRUE)
+            plot(x[[sm_meta$injectionOrder]],xsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order",
+                 ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nBefore correction")
+            points(x[[sm_meta$injectionOrder]][indpool],xsum[indpool],col="maroon",pch=16,cex=1.2)
+            plot(x[[sm_meta$injectionOrder]],Xnsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order",
+                 ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nAfter correction")
+            points(x[[sm_meta$injectionOrder]][indpool],Xnsum[indpool],col="maroon",pch=16,cex=1.2)
+            # PCA Plot before/after, normed only and ions plot
+            par(mfrow=c(3,4),ask=F,cex=1.2) 
+            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)}
+            # Before/after boxplot
+            par(mfrow=c(1,2),ask=F,cex=1.2) 
+            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 of pools before correction")
+              boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools 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 <- data.frame(ids)
+    idss[,i+1] <- as.character(idss[,i+1])
+    idss[which(is.na(idss[,i+1])),i+1] <- "no_modality"
+    idss[which(idss[,i+1]=="NA"),i+1] <- "no_modality"
+    idss[which(idss[,i+1]==""),i+1] <- "no_modality"
+    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)}
+}
+
+