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1 # Author: jfmartin
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2 # Modified by : mpetera
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3 ###############################################################################
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4 # Correction of analytical effects inter and intra batch on intensities using quality control pooled samples (QC-pools)
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5 # according to the algorithm mentioned by Van der Kloet (J Prot Res 2009).
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6 # Parameters : a dataframe of Ions intensities and an other of samples? metadata which must contains at least the three following columns :
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7 # "batch" to identify the batches of analyses ; need at least 3 QC-pools for linear adjustment and 8 for lo(w)ess adjustment
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8 # "injectionOrder" integer defining the injection order of all samples : QC-pools and analysed samples
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9 # "sampleType" indicates if defining a sample with "sample" or a QC-pool with "pool"
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10 # NO MISSING DATA are allowed
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11 # Version 0.91 insertion of ok_norm function to assess correction feasibility
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12 # Version 0.92 insertion of slope test in ok_norm
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13 # Version 0.93 name of log file define as a parameter of the correction function
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14 # 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)
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15 # Version 0.99 include non linear lowess correction.
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16 # Version 1.00 the corrected result matrix is return transposed in Galaxy
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17 # 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.
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18 # Version 1.02 plotsituation create a result file with the error code of non execution of correction set by function ok_norm
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19 # Version 1.03 fix bug in plot with "reg" option. suppression of ok_norm=4 condition if ok_norm function
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20 # Version 2.00 Addition of loess function, correction indicator, plots ; modification of returned objects' format, some plots' displays and ok_norm ifelse format
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21 # Version 2.01 Correction for pools negative values earlier in norm_QCpool
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22 # 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
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23 # Version 2.11 ok1 and ok2 permutation (ok_norm) ; conditional display of regression (plotsituation) ; grouping of linked lignes + conditioning (normX) ; conditioning for CVplot
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24 # Version 2.20 acplight function added from previous toolBox.R [# Version 1.01 "NA"-coding possibility added in acplight function]
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25 # 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
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26 # Version 2.90 change in handling of generated negative and Inf values
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27 # Version 2.91 Plot improvement
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28
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29 ok_norm=function(qcp,qci,spl,spi,method) {
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30 # Function used for one ion within one batch to determine whether or not batch correction is possible
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31 # ok_norm values :
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32 # 0 : no preliminary-condition problem
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33 # 1 : standard deviation of QC-pools or samples = 0
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34 # 2 : insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess)
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35 # 3 : significant difference between QC-pools' and samples' means
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36 # 4 : denominator =0 when on 1 pool per batch <> 0
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37 # 5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2
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38
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39 ok=0
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40 if (method=="linear") {minQC=3} else {minQC=8}
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41 if (length(qcp)<minQC) { ok=2
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42 } else {
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43 if (sd(qcp)==0 | sd(spl)==0) { ok=1
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44 } else {
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45 cvp= sd(qcp)/mean(qcp); cvs=sd(spl)/mean(spl)
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46 rttest=t.test(qcp,y=spl)
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47 reslsfit=lsfit(qci, qcp)
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48 reslsfitSample=lsfit(spl, spi)
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49 ordori=reslsfit$coefficients[1]
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50 penteB=reslsfit$coefficients[2]
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51 penteS=reslsfitSample$coefficients[2]
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52 # Significant difference between samples and pools
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53 if (rttest$p.value < 0.01) { ok=3
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54 } else {
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55 # to avoid denominator =0 when on 1 pool per batch <> 0
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56 if (method=="linear" & length(which(((penteB*qci)+ordori)==0))>0 ){ ok=6
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57 } else {
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58 # different sloop between samples and pools
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59 if (method=="linear" & penteB/penteS < -0.20) { ok=5 }
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60 }}}}
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61 ok_norm=ok
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62 }
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63
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64 plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none") {
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65 # Check for all ions in every batch if linear or lo(w)ess correction is possible.
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66 # Use ok_norm function and create a file (PreNormSummary.txt) with the error code.
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67 # Also create a pdf file with plots of linear and lo(w)ess regression lines.
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68 # x: dataframe with ions in columns and samples in rows ; x is the result of concatenation of sample metadata file and ions file
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69 # nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType"
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70 # outfic: name of regression plots pdf file
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71 # fact: factor to be used as categorical variable for plots and PCA.
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72 indfact =which(dimnames(x)[[2]]==fact)
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73 indtypsamp =which(dimnames(x)[[2]]=="sampleType")
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74 indbatch =which(dimnames(x)[[2]]=="batch")
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75 indinject =which(dimnames(x)[[2]]=="injectionOrder")
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76 lastIon=dim(x)[2]
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77 nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns
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78 nbb=length(levels(x$batch)) # Number of batch = number of levels of "batch" comlumn (factor)
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79 nbs=length(x$sampleType[x$sampleType=="sample"])# Number of samples = number of rows with "sample" value in sampleType
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80 pdf(outfic,width=27,height=7*ceiling((nbb+2)/3))
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81 cat(nbi," ions ",nbb," batch(es) \n")
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82 cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation de la dataset qui contiendra les CV
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83 pre_bilan=matrix(0,nrow=nbi,ncol=3*nbb) # dataset of ok_norm function results
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84 for (p in 1:nbi) {# for each ion
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85 par (mfrow=c(ceiling((nbb+2)/3),3),ask=F,cex=1.2)
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86 labion=dimnames(x)[[2]][p+nbid]
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87 indpool=which(x$sampleType=="pool") # QCpools subscripts in x
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88 pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1)/mean(pools1)# CV before correction
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89 for (b in 1:nbb) {# for each batch...
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90 xb=data.frame(x[(x$batch==levels(x$batch)[b]),c(indtypsamp,indinject,p+nbid)])
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91 indpb = which(xb$sampleType=="pool")# QCpools subscripts in the current batch
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92 indsp = which(xb$sampleType=="sample")# samples subscripts in the current batch
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93 indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool")# indices de tous les samples d'un batch pools+samples
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94 normLinearTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear")
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95 normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
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96 normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
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97 #cat(dimnames(x)[[2]][p+nbid]," batch ",b," loess ",normLoessTest," linear ",normLinearTest,"\n")
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98 pre_bilan[ p,3*b-2]=normLinearTest
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99 pre_bilan[ p,3*b-1]=normLoessTest
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100 pre_bilan[ p,3*b]=normLowessTest
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101 if(length(indpb)>1){
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102 if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span}
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103 resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct")
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104 resloessSample=loess(xb[indsp,3]~xb[indsp,2],span=2*length(indpool)/nbs,degree=2,family="gaussian",iterations=4,surface="direct")
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105 reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)
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106 reslowessSample=lowess(xb[indsp,2],xb[indsp,3])
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107 liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
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108 plot(xb[indsp,2],xb[indsp,3],pch=16, main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
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109 points(xb[indpb,2], xb[indpb,3],pch=5)
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110 points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")
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111 points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)
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112 points(reslowess,type="l",col="red"); points(reslowessSample,type="l",col="red",lty=2)
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113 abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue")
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114 abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")
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115 legend("topleft",c("pools","samples"),lty=c(1,2),bty="n")
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116 legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n")
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117 }
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118 }
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119 # series de plot avant et apres correction
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120 minval=min(x[p+nbid]);maxval=max(x[p+nbid])
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121 plot( x$injectionOrder, x[,p+nbid],col=x$batch,ylim=c(minval,maxval),ylab=labion,
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122 main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"))
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123 suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
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124 }
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125 dev.off()
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126 pre_bilan=data.frame(pre_bilan)
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127 labion=dimnames(x)[[2]][nbid+1:nbi]
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128 for (i in 1:nbb) {
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129 dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear")
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130 dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess")
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131 dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess")
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132 }
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133 bilan=data.frame(labion,pre_bilan)
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134 write.table(bilan,file=outres,sep="\t",row.names=F,quote=F)
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135 }
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136
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137
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138 normlowess=function (xb,detail="no",vref=1,b,span=NULL) {
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139 # Correction function applied to 1 ion in 1 batch. Use a lowess regression computed on QC-pools in order to correct samples intensity values
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140 # xb : dataframe for 1 ion in columns and samples in rows.
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141 # vref : reference value (average of ion)
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142 # b : batch subscript
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143 # nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType"
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144 indpb = which(xb$sampleType=="pool") # pools subscripts of current batch
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145 indsp = which(xb$sampleType=="sample") # samples of current batch subscripts
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146 indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool");# batch subscripts of all samples and QC-pools
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147 labion=dimnames(xb)[[2]][3]
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148 newval=xb[[3]] # initialisation of corrected values = intial values
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149 ind <- 0 # initialisation of correction indicator
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150 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
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151 #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
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152 if (normTodo==0) {
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153 if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span}
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154 reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools
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155 px=xb[indsp,2]; # vector of injectionOrder values only for samples
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156 for(j in 1:length(indbt)) {
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157 if (xb$sampleType[j]=="pool") {
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158 if (reslowess$y[which(indpb==j)]==0) reslowess$y[which(indpb==j)] <- 1
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159 newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)])}
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160 else { # for samples, the correction value cor correspond to the nearest QCpools
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161 cor= reslowess$y[which(abs(reslowess$x-px[which(indsp==j)])==min(abs(reslowess$x - px[which(indsp==j)])))]
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162 if (length(cor)>1) {cor=cor[1]}
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163 if (cor <= 0) {cor=vref} # no modification of initial value
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164 newval[j]=(vref*xb[j,3]) / cor
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165 }
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166 }
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167 if (detail=="reg") {
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168 liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
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169 plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
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170 points(xb[indpb,2], xb[indpb,3],pch=5)
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171 points(reslowess,type="l",col="red")
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172 }
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173 ind <- 1
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174 } else {# if ok_norm <> 0 , we perform a correction based on batch samples average
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175 moySample=mean(xb[indsp,3]);if (moySample==0) moySample=1
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176 newval[indsp] = (vref*xb[indsp,3])/moySample
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177 if(length(indpb)>0){
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178 moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
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179 newval[indpb] = (vref*xb[indpb,3])/moypool
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180 }
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181 }
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182 newval <- list(norm.ion=newval,norm.ind=ind)
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183 return(newval)
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184 }
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185
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186 normlinear <- function (xb,detail="no",vref=1,b,valneg=0) {
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187 # Correction function applied to 1 ion in 1 batch.
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188 # Use a linear regression computed on QC-pools in order to correct samples intensity values
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189 # xb: dataframe with ions in columns and samples in rows; x is a result of concatenation of sample metadata file and ion file
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190 # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder" and "sampleType"
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191 # b: which batch it is
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192 # valneg: to determine what to do with generated negative and Inf values
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193 indpb = which(xb$sampleType=="pool")# pools subscripts of current batch
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194 indsp = which(xb$sampleType=="sample")# samples of current batch subscripts
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195 indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool") # QCpools and samples of current batch subscripts
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196 labion=dimnames(xb)[[2]][3]
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197 newval=xb[[3]] # initialisation of corrected values = intial values
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198 ind <- 0 # initialisation of correction indicator
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199 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear")
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200 if (normTodo==0) {
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201 ind <- 1
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202 reslsfit=lsfit(xb[indpb,2],xb[indpb,3]) # linear regression for QCpools
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203 reslsfitSample=lsfit(xb[indsp,2],xb[indsp,3]) # linear regression for samples
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204 ordori=reslsfit$coefficients[1]
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205 pente=reslsfit$coefficients[2]
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206 if (detail=="reg") {
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207 liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
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208 plot(xb[indsp,2],xb[indsp,3],pch=16,
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209 main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
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210 points(xb[indpb,2], xb[indpb,3],pch=5)
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211 abline(reslsfit)
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212 abline(reslsfitSample,lty=2)
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213 }
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214 # correction with rescaling of ion global intensity (vref)
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215 newval = (vref*xb[indbt,3]) / (pente * (xb[indbt,2]) + ordori)
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216 newval[which((pente * (xb[indbt,2]) + ordori)<1)] <- -1 # to handle cases where 0<denominator<1
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217 # handling if any negative values (or null denominators)
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218 if(length(which((newval==Inf)|(newval<0)))!=0){
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219 toajust <- which((newval==Inf)|(newval<0))
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220 if(valneg=="NA"){
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221 newval[toajust] <- NA
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222 } else {
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223 newval[toajust] <- vref * (xb[indbt,3][toajust]) / mean(xb[indbt,3])
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224 ### Other possibility
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225 ## if(pente>0){ # slope orientation
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226 ## newval[toajust]<-(vref*(xb[indbt,3][toajust]))/(pente*ceiling(-ordori/pente+1.00001)+ordori)
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227 ## }else{
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228 ## newval[toajust]<-(vref*(xb[indbt,3][toajust]))/(pente*floor(-ordori/pente-1.00001)+ordori)
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229 ## }
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230 }
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231 }
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232 } else {# if ok_norm!=0 , we perform a correction based on batch samples average.
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233 moySample=mean(xb[indsp,3]); if (moySample==0) moySample=1
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234 newval[indsp] = (vref*xb[indsp,3])/moySample
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235 if(length(indpb)>0){
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236 moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
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237 newval[indpb] = (vref*xb[indpb,3])/moypool
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238 }
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239 }
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240 newval <- list(norm.ion=newval,norm.ind=ind)
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241 return(newval)
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242 }
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243
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244
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245 normloess <- function (xb,detail="no",vref=1,b,span=NULL) {
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246 # Correction function applied to 1 ion in 1 batch.
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247 # Use a loess regression computed on QC-pools in order to correct samples intensity values.
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248 # xb : dataframe for 1 ion in columns and samples in rows.
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249 # detail : level of detail in the outlog file.
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250 # vref : reference value (average of ion)
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251 # b : batch subscript
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252 indpb = which(xb$sampleType=="pool") # pools subscripts of current batch
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253 indsp = which(xb$sampleType=="sample") # samples of current batch subscripts
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254 indbt = which(xb$sampleType=="sample" | xb$sampleType=="pool");# batch subscripts of all samples and QCpools
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255 labion=dimnames(xb)[[2]][3]
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256 newval=xb[[3]] # initialisation of corrected values = intial values
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257 ind <- 0 # initialisation of correction indicator
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258 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
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259 #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
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260 if (normTodo==0) {
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261 if(length(span)==0){span1<-1}else{span1<-span}
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262 resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct") # loess regression with QCpools
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263 cor=predict(resloess,newdata=xb[,2])
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264 cor[cor<=1] <- 1
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265 newval=(vref*xb[,3]) / cor
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266 if(length(which(newval>3*(quantile(newval)[4])))>0){ # in this case no modification of initial value
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267 newval <- xb[,3]} else {ind <- 1} # confirmation of correction
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268 if ((detail=="reg")&(ind==1)) { # plot
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269 liminf=min(xb[indbt,3]);limsup=max(xb[indbt,3])
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270 plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup))
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271 points(xb[indpb,2], xb[indpb,3],pch=5)
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272 points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="red")
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273 }
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274 }
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275 if (ind==0) {# if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch samples average
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276 moySample=mean(xb[indsp,3]);if (moySample==0) moySample=1
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277 newval[indsp] = (vref*xb[indsp,3])/moySample
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278 if(length(indpb)>0){
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279 moypool=mean(xb[indpb,3]) ; if (moypool==0) moypool=1
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280 newval[indpb] = (vref*xb[indpb,3])/moypool
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281 }
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282 }
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283 newval <- list(norm.ion=newval,norm.ind=ind)
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284 return(newval)
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285 }
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286
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287
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288
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289 norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=F, NormInt=F, method="linear",span="none",valNull="0")
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290 {
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291 ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe.
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292 # x: dataframe with ions in column and samples' metadata
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293 # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType"
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294 # outlog: name of regression plots and PCA pdf file
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295 # fact: factor to be used as categorical variable for plots
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296 # metaion: dataframe of ions' metadata
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297 # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction.
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298 # detail="plot" with plot for all batch before and after correction.
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299 # detail="reg" with added plots with regression lines for all batches.
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300 # NormMoyPool: not used
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301 # NormInt: not used
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302 # method: regression method to be used to correct : "linear" or "lowess" or "loess"
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303 # valNull: to determine what to do with negatively estimated intensities
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304 indfact =which(dimnames(x)[[2]]==fact)
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305 indtypsamp=which(dimnames(x)[[2]]=="sampleType")
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306 indbatch =which(dimnames(x)[[2]]=="batch")
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307 indinject =which(dimnames(x)[[2]]=="injectionOrder")
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308 lastIon=dim(x)[2]
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309 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
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310 nbi=lastIon-nbid # number of ions
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311 nbb=length(levels(x$batch)) # Number of batch(es) = number of levels of factor "batch" (can be =1)
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312 nbs=length(x$sampleType[x$sampleType=="sample"])# Number of samples
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313 nbp=length(x$sampleType[x$sampleType=="pool"])# Number of QCpools
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314 Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nbp+nbs,ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe)
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315 dimnames(Xn)=dimnames(x)
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316 cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction
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317 dimnames(cv)[[2]]=c("avant","apres")
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318 if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"}
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319 pdf(outlog,width=27,height=20)
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320 cat(nbi," ions ",nbb," batch(es) \n")
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321 if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
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322 res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep="")))
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323 for (p in 1:nbi) {# for each ion
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324 labion=dimnames(x)[[2]][p+nbid]
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325 if (detail == "reg") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
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326 indpool=which(x$sampleType=="pool")# QCpools subscripts in all batches
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327 pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1)/mean(pools1)# CV before correction
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328 for (b in 1:nbb) {# for every batch
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329 indpb = which(x$batch==levels(x$batch)[b] & x$sampleType=="pool")# QCpools subscripts of the current batch
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330 indsp = which(x$batch==levels(x$batch)[b] & x$sampleType=="sample")# samples subscripts of the current batch
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331 indbt = which(x$batch==levels(x$batch)[b] & (x$sampleType=="pool" | x$sampleType=="sample")) # subscripts of all samples
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332 # cat(dimnames(x)[[2]][p+nbid]," indsp:",length(indsp)," indpb=",length(indpb)," indbt=",length(indbt)," ")
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333 sub=data.frame(x[(x$batch==levels(x$batch)[b]),c(indtypsamp,indinject,p+nbid)])
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334 if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull)
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335 } else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span)
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336 } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span)
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337 } else {stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n")}
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338 }}
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339 Xn[indbt,p+nbid] = res.norm[[1]]
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340 res.ind[p,b] <- res.norm[[2]]
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341 # CV batch test : if after normaliszation, CV before < CV after initial values are kept
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342 # moypoolRaw=mean(x[indpb,p+nbid]) ; if (moypoolRaw==0) moypoolRaw=1
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343 # moySampleRaw=mean(x[indsp,p+nbid]); if (moySampleRaw==0) moySampleRaw=1
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344 # moypool=mean(Xn[indpb,p+nbid]) ; if (moypool==0) moypool=1
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345 # #moySample=mean(Xn[indsp,p+nbid]); if (moySample==0) moySample=1
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346 # if (sd( Xn[indpb,p+nbid])/moypool>sd(x[indpb,p+nbid])/moypoolRaw) {
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347 # Xn[indpb,p+nbid] = (valref[p]*x[indpb,p+nbid])/moypoolRaw
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348 # Xn[indsp,p+nbid] = (valref[p]*x[indsp,p+nbid])/moySampleRaw
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349 # }
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350 }
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351 Xn[indpool,p+nbid][Xn[indpool,p+nbid]<0] <- 0
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352 pools2=Xn[indpool,p+nbid]; cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE)# CV apres correction
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353 if (detail=="reg" || detail=="plot" ) {
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354 # plot before and after correction
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355 minval=min(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE);maxval=max(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE)
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356 plot( x$injectionOrder, x[,p+nbid],col=x$batch,ylab=labion,ylim=c(minval,maxval),
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357 main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"))
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358 points(x$injectionOrder[indpool],x[indpool,p+nbid],col="maroon",pch=".",cex=2)
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359 plot(Xn$injectionOrder,Xn[,p+nbid],col=x$batch,ylab="",ylim=c(minval,maxval),
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360 main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"))
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361 points(Xn$injectionOrder[indpool],Xn[indpool,p+nbid],col="maroon",pch=".",cex=2)
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362 suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
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363 suppressWarnings(plot.design(Xn[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect after correction"))
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364 }
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365 }
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366 ### Replacement of post correction negative values by chosen value
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367 Xnn=Xn
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368 for (i in c((nbid+1):dim(Xn)[2])) {
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369 cneg=which(Xn[[i]]<0)
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370 Xnn[[i]]=replace(Xn[[i]],cneg,as.numeric(valNull))
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371 }
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372 Xn=Xnn
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373
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374 if (detail=="reg" || detail=="plot" || detail=="no") {
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375 if (nbi > 3) {
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376 par(mfrow=c(3,4),ask=F,cex=1.2) # PCA Plot before/after, normed only and ions plot
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377 acplight(x[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE)
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378 norm.ion <- which(colnames(Xn)%in%(rownames(res.ind)[which(rowSums(res.ind)>=1)]))
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379 acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE,norm.ion)
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380 if(length(norm.ion)>0){acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,norm.ion)],"uv",TRUE)}
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381 par(mfrow=c(1,2),ask=F,cex=1.2) # Before/after boxplot
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382 cvplot=cv[!is.na(cv[[1]])&!is.na(cv[[2]]),]
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383 if(nrow(cvplot)>0){
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384 boxplot(cvplot[[1]],ylim=c(min(cvplot),max(cvplot)),main="CV before correction")
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385 boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV after correction")
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386 }
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387 dev.off()
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388 }
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389 }
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390 if (nbi<=3) {dev.off()}
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391 # transposed matrix is return (format of the initial matrix with ions in rows)
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392 Xr=Xn[,-c(1:nbid)]; dimnames(Xr)[[1]]=Xn[[1]]
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393 Xr=t(Xr) ; Xr <- data.frame(ions=rownames(Xr),Xr)
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394
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395 res.norm[[1]] <- Xr ; res.norm[[2]] <- data.frame(metaion,res.ind) ; res.norm[[3]] <- x[,c(1:nbid)]
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396 names(res.norm) <- c("dataMatrix","variableMetadata","sampleMetadata")
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397 return(res.norm)
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398 }
|
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399
|
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400
|
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401
|
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402
|
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403
|
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404 acplight <- function(ids, scaling="uv", indiv=FALSE,indcol=NULL) {
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405 suppressPackageStartupMessages(library(ade4))
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406 suppressPackageStartupMessages(library(pcaMethods))
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407 # Make a PCA and plot scores and loadings.
|
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408 # First column must contain samples' identifiers.
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409 # Columns 2 to 4 contain factors to colour the plots.
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410 for (i in 1:3) {
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411 idss=ids[which(ids[,i+1]!="NA"),]
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412 idss=data.frame(idss[idss[,i+1]!="",])
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413 classe=as.factor(idss[[i+1]])
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414 idsample=as.character(idss[[1]])
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415 colour=1:length(levels(classe))
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416 ions=as.matrix(idss[,5:dim(idss)[2]])
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417 # Removing ions containing NA (not compatible with standard PCA)
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418 ions=t(na.omit(t(ions)))
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419 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")}}
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420 # Scaling choice: "uv","none","pareto"
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421 object=suppressWarnings(prep(ions, scale=scaling, center=TRUE))
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422 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")}}
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423 # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F
|
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424 result <- pca(object, center=F, method="svd", nPcs=2)
|
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425 # ADE4 : to plot samples' ellipsoid for each class
|
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426 s.class(result@scores, classe, cpoint = 1,xax=1,yax=2,col=colour,sub=sprintf("Scores - PCs %sx%s",1,2), possub="bottomright")
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427 #s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright")
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428 if(i==1){resulti <- result}
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429 }
|
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430 if(indiv) {
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431 colour <- rep("darkblue",length(resulti@loadings)) ; if(!is.null(indcol)) {colour[-c(indcol)] <- "red"}
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432 plot(resulti@loadings,col=colour,main="Loadings",xaxt="n",yaxt="n",pch=20,
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433 xlab=bquote(PC1-R^2==.(resulti@R2[1])),ylab=bquote(PC2 - R^2 == .(resulti@R2[2])))
|
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434 abline(h=0,v=0)}
|
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435 }
|
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436
|
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437
|
