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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 # Version 3.00 - handling of sample tags' parameters
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29 # - accepting sample types beyond "pool" and "sample"
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30 # - dealing with NA
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31 # - changes in the normalisation strategy regarding mean values to adjust for NA or 0 values
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32 # - changes in the normalisation strategy regarding unconsistant values (negative or Inf)
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33
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34 ok_norm=function(qcp,qci,spl,spi,method,normref=NA,valimp="0") {
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35 # Function used for one ion within one batch to determine whether or not batch correction is possible
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36 # ok_norm values :
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37 # 0 : no preliminary-condition problem
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38 # 1 : standard deviation of QC-pools or samples = 0
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39 # 2 : insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess)
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40 # 2.5 : less than 2 samples within a batch
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41 # 3 : significant difference between QC-pools' and samples' means
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42 # 4 : denominator =0 when on 1 pool per batch <> 0
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43 # 5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2
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44 # 6 : (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA
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45 # Parameters:
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46 # qcp: intensity of a given ion for pools
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47 # qci: injection numbers for pools
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48 # spl: intensity of a given ion for samples
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49 # spi: injection numbers for samples
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50 # method: to provide specific checks for "linear"
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51
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52 ok=0
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53 if (method=="linear") {minQC=3} else {minQC=8}
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54 if (length(qcp[!is.na(qcp)])<minQC) { ok=2 } else { if (length(spl[!is.na(spl)])<2) { ok=2.5
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55 } else {
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56 if (sd(qcp,na.rm=TRUE)==0 | sd(spl,na.rm=TRUE)==0) { ok=1
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57 } else {
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58 cvp= sd(qcp,na.rm=TRUE)/mean(qcp,na.rm=TRUE); cvs=sd(spl,na.rm=TRUE)/mean(spl,na.rm=TRUE)
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59 rttest=t.test(qcp,y=spl)
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60 reslsfit=lsfit(qci, qcp)
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61 reslsfitSample=lsfit(spl, spi)
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62 ordori=reslsfit$coefficients[1]
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63 penteB=reslsfit$coefficients[2]
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64 penteS=reslsfitSample$coefficients[2]
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65 # Significant difference between samples and pools
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66 if (rttest$p.value < 0.01) { ok=3
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67 } else {
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68 # to avoid denominator =0 when on 1 pool per batch <> 0
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69 if (method=="linear" & length(which(((penteB*qci)+ordori)==0))>0 ){ ok=6
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70 } else {
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71 # different sloop between samples and pools
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72 if (method=="linear" & penteB/penteS < -0.20) { ok=5
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73 } else {
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74 #
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75 if (method=="linear" & !is.na(normref) & valimp=="NA") {
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76 denom = (penteB * c(spi,qci) + ordori)
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77 normval = c(spl,qcp)*normref / denom
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78 if(length(which((normval==Inf)|(denom<1)))==length(normval)){ok=6}
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79 }
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80 }}}}}}
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81 ok_norm=ok
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82 }
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83
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84 plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none",
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85 sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType",
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86 sampleTag=list(pool="pool",blank="blank",sample="sample"))) {
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87 # Checks for all ions in every batch if linear or lo(w)ess correction is possible.
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88 # Uses ok_norm function and creates a file (PreNormSummary.txt) with the corresponding error codes.
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89 # Also creates a pdf file with plots of linear and lo(w)ess regression lines.
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90 # Parameters:
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91 # 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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92 # nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType"
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93 # outfic: name of regression plots pdf file
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94 # outres: name of summary table file
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95 # fact: factor to be used as categorical variable for plots and PCA
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96 # span: span value for lo(w)ess regression; "none" for linear or default values
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97 # sm_meta: list of information about sample metadata coding
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98 indfact=which(dimnames(x)[[2]]==fact)
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99 indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType)
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100 indbatch=which(dimnames(x)[[2]]==sm_meta$batch)
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101 indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder)
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102 lastIon=dim(x)[2]
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103 nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns
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104 nbb=length(levels(x[[sm_meta$batch]])) # Number of batch = number of levels of "batch" comlumn (factor)
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105 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
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106 pdf(outfic,width=27,height=7*ceiling((nbb+2)/3))
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107 cat(nbi," ions ",nbb," batch(es) \n")
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108 cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation de la dataset qui contiendra les CV
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109 pre_bilan=matrix(0,nrow=nbi,ncol=3*nbb) # dataset of ok_norm function results
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110 for (p in 1:nbi) {# for each ion
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111 par (mfrow=c(ceiling((nbb+2)/3),3),ask=F,cex=1.2)
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112 labion=dimnames(x)[[2]][p+nbid]
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113 indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in x
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114 pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction
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115 for (b in 1:nbb) {# for each batch...
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116 xb=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)])
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117 indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in the current batch
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118 indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples subscripts in the current batch
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119 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")
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120 normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
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121 normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
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122 pre_bilan[ p,3*b-2]=normLinearTest
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123 pre_bilan[ p,3*b-1]=normLoessTest
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124 pre_bilan[ p,3*b]=normLowessTest
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125 if(length(indpb)>1){
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126 if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span}
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127 if(normLoessTest!=2){resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct")}
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128 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") }
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129 if(normLowessTest!=2){reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)}
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130 if(length(which(!(is.na(xb[indsp,3]))))>1){reslowessSample=lowess(xb[indsp,2],xb[indsp,3])}
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131 liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
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132 firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
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133 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))
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134 if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18,col="grey")}
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135 points(xb[indpb,2], xb[indpb,3],pch=5)
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136 if(normLoessTest!=2){points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")}
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137 if(length(which(!(is.na(xb[indsp,3]))))>1){points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)}
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138 if(normLowessTest!=2){points(reslowess,type="l",col="red")}; if(length(which(!(is.na(xb[indsp,3]))))>1){points(reslowessSample,type="l",col="red",lty=2)}
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139 abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue")
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140 if(length(which(!(is.na(xb[indsp,3]))))>1){abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")}
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141 legend("topleft",c("pools","samples"),lty=c(1,2),bty="n")
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142 legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n")
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143 } else {
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144 plot.new()
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145 legend("center","Plot only available when the\nbatch contains at least 2 pools.")
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146 }
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147 }
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148 # series de plot avant correction
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149 minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE)
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150 plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylim=c(minval,maxval),ylab=labion,
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151 main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order")
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152 suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
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153 }
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154 dev.off()
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155 pre_bilan=data.frame(pre_bilan)
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156 labion=dimnames(x)[[2]][nbid+1:nbi]
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157 for (i in 1:nbb) {
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158 dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear")
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159 dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess")
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160 dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess")
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161 }
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162 bilan=data.frame(labion,pre_bilan)
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163 write.table(bilan,file=outres,sep="\t",row.names=F,quote=F)
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164 }
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165
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166
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167 normlowess=function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType",
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168 sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
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169 # Correction function applied to 1 ion in 1 batch.
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170 # Uses a lowess regression computed on QC-pools in order to correct samples intensity values
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171 # xb: dataframe for 1 ion in columns and samples in rows.
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172 # vref: reference value (average of ion)
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173 # b: batch subscript
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174 # detail: level of detail in the outlog file
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175 # span: span value for lo(w)ess regression; NULL for default values
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176 # valneg: to determine what to do with generated negative and Inf values
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177 # sm_meta: list of information about sample metadata coding
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178 # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
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179 indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch
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180 indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts
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181 labion=dimnames(xb)[[2]][3]
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182 newval=xb[[3]] # initialisation of corrected values = intial values
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183 ind <- 0 # initialisation of correction indicator
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184 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess")
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185 #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n")
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186 if (normTodo==0) {
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187 if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span}
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188 reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools
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189 if(length(which(reslowess$y<min_norm))!=0){ # to handle cases where 0<denominator<min_norm or negative
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190 toajust <- which(reslowess$y<min_norm)
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191 if(valneg=="NA"){ reslowess$y[toajust] <- NA
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192 } else { if(valneg=="0"){ reslowess$y[toajust] <- -1
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193 } else {
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194 mindenom <- min(reslowess$y[reslowess$y>=min_norm],na.rm=TRUE)
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195 reslowess$y[toajust] <- mindenom
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196 } } }
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197 for(j in 1:nrow(xb)) {
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198 if (j %in% indpb) {
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199 newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)])
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200 } else { # for samples other than pools, the correction value "corv" correspond to the nearest QCpools
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201 corv= reslowess$y[which(abs(reslowess$x-xb[j,2])==min(abs(reslowess$x-xb[j,2]),na.rm=TRUE))]
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202 if (length(corv)>1) {corv=corv[1]}
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203 newval[j]=(vref*xb[j,3]) / corv
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204 }
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205 if((!is.na(newval[j]))&(newval[j]<0)){newval[j]<-0}
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206 }
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207 if (detail=="reg") {
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208 liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
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209 firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
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210 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))
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211 if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
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212 points(xb[indpb,2], xb[indpb,3],pch=5)
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213 points(reslowess,type="l",col="red")
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214 }
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215 ind <- 1
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216 } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average
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217 if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
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218 moypool=mean(xb[indpb,3],na.rm=TRUE)
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219 newval = (vref*xb[,3])/moypool
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220 } else {
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221 moysamp=mean(xb[indsp,3],na.rm=TRUE)
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222 if((!is.na(moysamp))&(moysamp>0)){
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223 cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
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224 newval = (vref*xb[,3])/moysamp
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225 } else {
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226 dev.off()
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227 stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
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228 }
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229 }
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230 }
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231 newval <- list(norm.ion=newval,norm.ind=ind)
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232 return(newval)
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233 }
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234
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235 normlinear <- function (xb,detail="no",vref=1,b,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType",
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236 sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
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237 # Correction function applied to 1 ion in 1 batch.
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238 # Uses a linear regression computed on QC-pools in order to correct samples intensity values
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239 # 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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240 # detail: level of detail in the outlog file
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241 # vref: reference value (average of ion)
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242 # b: which batch it is
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243 # valneg: to determine what to do with generated negative and Inf values
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244 # sm_meta: list of information about sample metadata coding
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245 # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
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246 indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# pools subscripts of current batch
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247 indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples of current batch subscripts
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248 labion=dimnames(xb)[[2]][3]
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249 newval=xb[[3]] # initialisation of corrected values = intial values
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250 ind <- 0 # initialisation of correction indicator
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251 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=vref,valimp=valneg)
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252 if (normTodo==0) {
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253 ind <- 1
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254 reslsfit=lsfit(xb[indpb,2],xb[indpb,3]) # linear regression for QCpools
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255 reslsfitSample=lsfit(xb[indsp,2],xb[indsp,3]) # linear regression for samples
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256 ordori=reslsfit$coefficients[1]
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257 pente=reslsfit$coefficients[2]
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258 if (detail=="reg") {
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259 liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
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260 firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
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261 plot(xb[indsp,2],xb[indsp,3],pch=16,
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262 main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj))
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263 if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
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264 points(xb[indpb,2], xb[indpb,3],pch=5)
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265 abline(reslsfit)
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266 abline(reslsfitSample,lty=2)
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267 }
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268 # correction with rescaling of ion global intensity (vref)
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269 newval = (vref*xb[,3]) / (pente * (xb[,2]) + ordori)
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270 newval[which((pente * (xb[,2]) + ordori)<min_norm)] <- -1 # to handle cases where 0<denominator<1 or negative
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271 # handling if any negative values
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272 if(length(which((newval==Inf)|(newval<0)))!=0){
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273 toajust <- which((newval==Inf)|(newval<0))
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274 if(valneg=="NA"){ newval[toajust] <- NA
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275 } else { if(valneg=="0"){ newval[toajust] <- 0
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276 } else {
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277 mindenom <- (pente * (xb[,2]) + ordori)
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278 mindenom <- min(mindenom[mindenom>=min_norm],na.rm=TRUE)
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279 newval[toajust] <- vref * (xb[,3][toajust]) / mindenom
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280 }
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281 }
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282 }
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283 } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average
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284 if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
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285 moypool=mean(xb[indpb,3],na.rm=TRUE)
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286 newval = (vref*xb[,3])/moypool
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287 } else {
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288 moysamp=mean(xb[indsp,3],na.rm=TRUE)
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289 if((!is.na(moysamp))&(moysamp>0)){
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290 cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
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291 newval = (vref*xb[,3])/moysamp
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292 } else {
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293 dev.off()
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294 stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
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295 }
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296 }
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297 }
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298 newval <- list(norm.ion=newval,norm.ind=ind)
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299 return(newval)
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300 }
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301
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302
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303 normloess <- function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType",
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304 sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){
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305 # Correction function applied to 1 ion in 1 batch.
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306 # Uses a loess regression computed on QC-pools in order to correct samples intensity values.
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307 # xb: dataframe for 1 ion in columns and samples in rows.
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308 # detail: level of detail in the outlog file.
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309 # vref: reference value (average of ion)
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310 # b: batch subscript
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311 # span: span value for lo(w)ess regression; NULL for default values
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312 # valneg: to determine what to do with generated negative and Inf values
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313 # sm_meta: list of information about sample metadata coding
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314 # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
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315 indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch
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316 indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts
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317 indbt = which(xb[[sm_meta$sampleType]] %in% c(sm_meta$sampleTag$sample,sm_meta$sampleTag$pool))# batch subscripts of samples and QCpools
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318 labion=dimnames(xb)[[2]][3]
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319 newval=xb[[3]] # initialisation of corrected values = intial values
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320 ind <- 0 # initialisation of correction indicator
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321 normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess")
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322 if (normTodo==0) {
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323 if(length(span)==0){span1<-1}else{span1<-span}
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324 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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325 corv=predict(resloess,newdata=xb[,2])
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326 if(length(which(corv<min_norm))!=0){ # unconsistant values handling
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327 toajust <- which(corv<min_norm)
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328 if(valneg=="NA"){ corv[toajust] <- NA
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329 } else { if(valneg=="0"){ corv[toajust] <- -1
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330 } else {
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331 mindenom <- min(corv[corv>=min_norm],na.rm=TRUE)
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332 corv[toajust] <- mindenom
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333 }
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334 }
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335 }
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336 newvalps=(vref*xb[indbt,3]) / corv[indbt] # to check if correction generates outlier values
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337 refthresh=max(c(3*(quantile(newvalps,na.rm=TRUE)[4]),1.3*(xb[indbt,3])),na.rm=TRUE)
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338 if(length(which(newvalps>refthresh))>0){ # if outliers
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339 # in this case no modification of initial value
|
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340 newval <- xb[,3]
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341 } else {
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|
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342 newval=(vref*xb[,3]) / corv
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|
|
343 newval[newval<0] <- 0
|
|
|
344 ind <- 1 # confirmation of correction
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|
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345 }
|
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346 if ((detail=="reg")&(ind==1)) { # plot
|
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347 liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE)
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348 firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE)
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349 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))
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350 if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)}
|
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351 points(xb[indpb,2], xb[indpb,3],pch=5)
|
|
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352 points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="red")
|
|
|
353 }
|
|
|
354 }
|
|
|
355 if (ind==0) {# if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch pool or sample average
|
|
|
356 if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){
|
|
|
357 moypool=mean(xb[indpb,3],na.rm=TRUE)
|
|
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358 newval = (vref*xb[,3])/moypool
|
|
|
359 } else {
|
|
|
360 moysamp=mean(xb[indsp,3],na.rm=TRUE)
|
|
|
361 if((!is.na(moysamp))&(moysamp>0)){
|
|
|
362 cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n")
|
|
|
363 newval = (vref*xb[,3])/moysamp
|
|
|
364 } else {
|
|
|
365 dev.off()
|
|
|
366 stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n"))
|
|
|
367 }
|
|
|
368 }
|
|
|
369 }
|
|
|
370 newval <- list(norm.ion=newval,norm.ind=ind)
|
|
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371 return(newval)
|
|
|
372 }
|
|
|
373
|
|
|
374
|
|
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375
|
|
|
376 norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=FALSE, NormInt=FALSE, method="linear",span="none",valNull="0",
|
|
|
377 sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType",
|
|
|
378 sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1) {
|
|
|
379 ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe.
|
|
|
380 # x: dataframe with ions in column and samples' metadata
|
|
|
381 # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType"
|
|
|
382 # outlog: name of regression plots and PCA pdf file
|
|
|
383 # fact: factor to be used as categorical variable for plots
|
|
|
384 # metaion: dataframe of ions' metadata
|
|
|
385 # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction.
|
|
|
386 # detail="plot" with plot for all batch before and after correction.
|
|
|
387 # detail="reg" with added plots with regression lines for all batches.
|
|
|
388 # NormMoyPool: not used
|
|
|
389 # NormInt: not used
|
|
|
390 # method: regression method to be used to correct : "linear" or "lowess" or "loess"
|
|
|
391 # span: span value for lo(w)ess regression; "none" for linear or default values
|
|
|
392 # valNull: to determine what to do with negatively estimated intensities
|
|
|
393 # sm_meta: list of information about sample metadata coding
|
|
|
394 # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive
|
|
|
395 indfact=which(dimnames(x)[[2]]==fact)
|
|
|
396 indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType)
|
|
|
397 indbatch=which(dimnames(x)[[2]]==sm_meta$batch)
|
|
|
398 indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder)
|
|
|
399 lastIon=dim(x)[2]
|
|
|
400 indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in all batches
|
|
|
401 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
|
|
|
402 nbi=lastIon-nbid # number of ions
|
|
|
403 nbb=length(levels(x[[sm_meta$batch]])) # Number of batch(es) = number of levels of factor "batch" (can be =1)
|
|
|
404 Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nrow(x),ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe)
|
|
|
405 dimnames(Xn)=dimnames(x)
|
|
|
406 cv=data.frame(matrix(NA,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction
|
|
|
407 dimnames(cv)[[2]]=c("avant","apres")
|
|
|
408 if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"}
|
|
|
409 pdf(outlog,width=27,height=20)
|
|
|
410 cat(nbi," ions ",nbb," batch(es) \n")
|
|
|
411 if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}}
|
|
|
412 res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep="")))
|
|
|
413 for (p in 1:nbi) {# for each ion
|
|
|
414 labion=dimnames(x)[[2]][p+nbid]
|
|
|
415 pools1=x[indpool,p+nbid]
|
|
|
416 if(length(which(pools1[!(is.na(pools1))]>0))<2){ # if not enough pools >0 -> no normalisation
|
|
|
417 war.note <- paste("Warning: less than 2 pools with values >0 in",labion,"-> no normalisation for this ion.")
|
|
|
418 cat(war.note,"\n")
|
|
|
419 Xn[,p+nbid] <- x[,p+nbid]
|
|
|
420 res.ind[p,] <- rep(0,nbb)
|
|
|
421 if (detail=="reg" || detail=="plot" ) {
|
|
|
422 par(mfrow=c(2,2),ask=F,cex=1.5)
|
|
|
423 plot.new()
|
|
|
424 legend("center",war.note)
|
|
|
425 minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE)
|
|
|
426 plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval),
|
|
|
427 main="No correction",xlab="injection order")
|
|
|
428 points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1)
|
|
|
429 }
|
|
|
430 } else {
|
|
|
431 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)}}
|
|
|
432 if (detail == "plot") {par(mfrow=c(2,2),ask=F,cex=1.5)}
|
|
|
433 cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction
|
|
|
434 for (b in 1:nbb) {# for every batch
|
|
|
435 indbt = which(x[[sm_meta$batch]]==(levels(x[[sm_meta$batch]])[b])) # subscripts of all samples
|
|
|
436 sub=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)])
|
|
|
437 if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull,sm_meta,min_norm)
|
|
|
438 } else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm)
|
|
|
439 } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm)
|
|
|
440 } else {stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n")}
|
|
|
441 }}
|
|
|
442 Xn[indbt,p+nbid] = res.norm[[1]]
|
|
|
443 res.ind[p,b] <- res.norm[[2]]
|
|
|
444 }
|
|
|
445 # Post correction CV calculation
|
|
|
446 pools2=Xn[indpool,p+nbid]
|
|
|
447 cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE)
|
|
|
448 if (detail=="reg" || detail=="plot" ) {
|
|
|
449 # plot before and after correction
|
|
|
450 minval=min(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE);maxval=max(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE)
|
|
|
451 plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval),
|
|
|
452 main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order")
|
|
|
453 points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1)
|
|
|
454 plot(Xn[[sm_meta$injectionOrder]],Xn[,p+nbid],col=x[[sm_meta$batch]],ylab="",ylim=c(minval,maxval),
|
|
|
455 main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"),xlab="injection order")
|
|
|
456 points(Xn[[sm_meta$injectionOrder]][indpool],Xn[indpool,p+nbid],col="maroon",pch=16,cex=1)
|
|
|
457 suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction"))
|
|
|
458 suppressWarnings(plot.design(Xn[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect after correction"))
|
|
|
459 }
|
|
|
460 }
|
|
|
461 }
|
|
|
462
|
|
|
463 if (detail=="reg" || detail=="plot" || detail=="no") {
|
|
|
464 if (nbi > 3) {
|
|
|
465 # Sum of ions before/after plot
|
|
|
466 par(mfrow=c(1,2),ask=F,cex=1.2)
|
|
|
467 xsum <- rowSums(x[,(nbid+1):lastIon],na.rm=TRUE)
|
|
|
468 Xnsum <- rowSums(Xn[,(nbid+1):lastIon],na.rm=TRUE)
|
|
|
469 plot(x[[sm_meta$injectionOrder]],xsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order",
|
|
|
470 ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nBefore correction")
|
|
|
471 points(x[[sm_meta$injectionOrder]][indpool],xsum[indpool],col="maroon",pch=16,cex=1.2)
|
|
|
472 plot(x[[sm_meta$injectionOrder]],Xnsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order",
|
|
|
473 ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nAfter correction")
|
|
|
474 points(x[[sm_meta$injectionOrder]][indpool],Xnsum[indpool],col="maroon",pch=16,cex=1.2)
|
|
|
475 # PCA Plot before/after, normed only and ions plot
|
|
|
476 par(mfrow=c(3,4),ask=F,cex=1.2)
|
|
|
477 acplight(x[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE)
|
|
|
478 norm.ion <- which(colnames(Xn)%in%(rownames(res.ind)[which(rowSums(res.ind)>=1)]))
|
|
|
479 acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE,norm.ion)
|
|
|
480 if(length(norm.ion)>0){acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,norm.ion)],"uv",TRUE)}
|
|
|
481 # Before/after boxplot
|
|
|
482 par(mfrow=c(1,2),ask=F,cex=1.2)
|
|
|
483 cvplot=cv[!is.na(cv[[1]])&!is.na(cv[[2]]),]
|
|
|
484 if(nrow(cvplot)>0){
|
|
|
485 boxplot(cvplot[[1]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools before correction")
|
|
|
486 boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools after correction")
|
|
|
487 }
|
|
|
488 dev.off()
|
|
|
489 }
|
|
|
490 }
|
|
|
491 if (nbi<=3) {dev.off()}
|
|
|
492 # transposed matrix is return (format of the initial matrix with ions in rows)
|
|
|
493 Xr=Xn[,-c(1:nbid)]; dimnames(Xr)[[1]]=Xn[[1]]
|
|
|
494 Xr=t(Xr) ; Xr <- data.frame(ions=rownames(Xr),Xr)
|
|
|
495
|
|
|
496 res.norm[[1]] <- Xr ; res.norm[[2]] <- data.frame(metaion,res.ind) ; res.norm[[3]] <- x[,c(1:nbid)]
|
|
|
497 names(res.norm) <- c("dataMatrix","variableMetadata","sampleMetadata")
|
|
|
498 return(res.norm)
|
|
|
499 }
|
|
|
500
|
|
|
501
|
|
|
502
|
|
|
503
|
|
|
504
|
|
|
505 acplight <- function(ids, scaling="uv", indiv=FALSE,indcol=NULL) {
|
|
|
506 suppressPackageStartupMessages(library(ade4))
|
|
|
507 suppressPackageStartupMessages(library(pcaMethods))
|
|
|
508 # Make a PCA and plot scores and loadings.
|
|
|
509 # First column must contain samples' identifiers.
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510 # Columns 2 to 4 contain factors to colour the plots.
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511 for (i in 1:3) {
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512 idss <- data.frame(ids)
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513 idss[,i+1] <- as.character(idss[,i+1])
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514 idss[which(is.na(idss[,i+1])),i+1] <- "no_modality"
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515 idss[which(idss[,i+1]=="NA"),i+1] <- "no_modality"
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516 idss[which(idss[,i+1]==""),i+1] <- "no_modality"
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517 classe=as.factor(idss[[i+1]])
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518 idsample=as.character(idss[[1]])
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519 colour=1:length(levels(classe))
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520 ions=as.matrix(idss[,5:dim(idss)[2]])
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521 # Removing ions containing NA (not compatible with standard PCA)
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522 ions=t(na.omit(t(ions)))
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523 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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524 # Scaling choice: "uv","none","pareto"
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525 object=suppressWarnings(prep(ions, scale=scaling, center=TRUE))
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526 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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527 # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F
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528 result <- pca(object, center=F, method="svd", nPcs=2)
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529 # ADE4 : to plot samples' ellipsoid for each class
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530 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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531 #s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright")
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532 if(i==1){resulti <- result}
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533 }
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534 if(indiv) {
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535 colour <- rep("darkblue",length(resulti@loadings)) ; if(!is.null(indcol)) {colour[-c(indcol)] <- "red"}
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536 plot(resulti@loadings,col=colour,main="Loadings",xaxt="n",yaxt="n",pch=20,
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537 xlab=bquote(PC1-R^2==.(resulti@R2[1])),ylab=bquote(PC2 - R^2 == .(resulti@R2[2])))
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538 abline(h=0,v=0)}
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539 }
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540
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541
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