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annotate lib.r @ 16:20a75ba4345b draft

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planemo upload for repository https://github.com/workflow4metabolomics/xcms commit 22c4e92909198328fc7439ff47e4546a273eb907
author lecorguille
date Sun, 05 Feb 2017 08:57:02 -0500
parents c04568596f40
children 3bd1e74d4abc
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1 #Authors ABiMS TEAM
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2 #Lib.r for Galaxy Workflow4Metabolomics xcms tools
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3 #
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4 #version 2.4: lecorguille
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5 # add getPeaklistW4M
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6 #version 2.3: yguitton
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7 # correction for empty PDF when only 1 class
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8 #version 2.2
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9 # correct bug in Base Peak Chromatogram (BPC) option, not only TIC when scanrange used in xcmsSet
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10 # Note if scanrange is used a warning is prompted in R console but do not stop PDF generation
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11 #version 2.1: yguitton
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12 # Modifications made by Guitton Yann
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15 #@author G. Le Corguille
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16 #This function convert if it is required the Retention Time in minutes
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17 RTSecondToMinute <- function(variableMetadata, convertRTMinute) {
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18 if (convertRTMinute){
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19 #converting the retention times (seconds) into minutes
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20 print("converting the retention times into minutes in the variableMetadata")
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21 variableMetadata[,"rt"]=variableMetadata[,"rt"]/60
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22 variableMetadata[,"rtmin"]=variableMetadata[,"rtmin"]/60
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23 variableMetadata[,"rtmax"]=variableMetadata[,"rtmax"]/60
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24 }
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25 return (variableMetadata)
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26 }
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28 #@author G. Le Corguille
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29 #This function format ions identifiers
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30 formatIonIdentifiers <- function(dataData, numDigitsRT=0, numDigitsMZ=0) {
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31 return(make.unique(paste0("M",round(dataData[,"mz"],numDigitsMZ),"T",round(dataData[,"rt"],numDigitsRT))))
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32 }
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34 #@author G. Le Corguille
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35 # value: intensity values to be used into, maxo or intb
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36 getPeaklistW4M <- function(xset, intval="into",convertRTMinute=F,numDigitsMZ=4,numDigitsRT=0,variableMetadataOutput,dataMatrixOutput) {
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37 groups <- xset@groups
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38 values <- groupval(xset, "medret", value=intval)
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40 # renamming of the column rtmed to rt to fit with camera peaklist function output
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41 colnames(groups)[colnames(groups)=="rtmed"] <- "rt"
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42 colnames(groups)[colnames(groups)=="mzmed"] <- "mz"
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44 ids <- formatIonIdentifiers(groups, numDigitsRT=numDigitsRT, numDigitsMZ=numDigitsMZ)
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45 groups = RTSecondToMinute(groups, convertRTMinute)
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47 rownames(groups) = ids
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48 rownames(values) = ids
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50 #@TODO: add "name" as the first column name
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51 #colnames(groups)[1] = "name"
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52 #colnames(values)[1] = "name"
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54 write.table(groups, file=variableMetadataOutput,sep="\t",quote=F,row.names = T,col.names = NA)
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55 write.table(values, file=dataMatrixOutput,sep="\t",quote=F,row.names = T,col.names = NA)
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56 }
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58 #@author Y. Guitton
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59 getBPC <- function(file,rtcor=NULL, ...) {
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60 object <- xcmsRaw(file)
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61 sel <- profRange(object, ...)
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62 cbind(if (is.null(rtcor)) object@scantime[sel$scanidx] else rtcor ,xcms:::colMax(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
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63 #plotChrom(xcmsRaw(file), base=T)
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64 }
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66 #@author Y. Guitton
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67 getBPCs <- function (xcmsSet=NULL, pdfname="BPCs.pdf",rt=c("raw","corrected"), scanrange=NULL) {
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68 cat("Creating BIC pdf...\n")
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70 if (is.null(xcmsSet)) {
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71 cat("Enter an xcmsSet \n")
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72 stop()
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73 } else {
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74 files <- filepaths(xcmsSet)
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75 }
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77 phenoDataClass<-as.vector(levels(xcmsSet@phenoData[,1])) #sometime phenoData have more than 1 column use first as class
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79 classnames<-vector("list",length(phenoDataClass))
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80 for (i in 1:length(phenoDataClass)){
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81 classnames[[i]]<-which( xcmsSet@phenoData[,1]==phenoDataClass[i])
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82 }
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84 N <- dim(phenoData(xcmsSet))[1]
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86 TIC <- vector("list",N)
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89 for (j in 1:N) {
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91 TIC[[j]] <- getBPC(files[j])
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92 #good for raw
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93 # seems strange for corrected
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94 #errors if scanrange used in xcmsSetgeneration
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95 if (!is.null(xcmsSet) && rt == "corrected")
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96 rtcor <- xcmsSet@rt$corrected[[j]]
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97 else
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98 rtcor <- NULL
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99
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100 TIC[[j]] <- getBPC(files[j],rtcor=rtcor)
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101 # TIC[[j]][,1]<-rtcor
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102 }
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103
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104
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105
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106 pdf(pdfname,w=16,h=10)
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107 cols <- rainbow(N)
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108 lty = 1:N
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109 pch = 1:N
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110 #search for max x and max y in BPCs
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111 xlim = range(sapply(TIC, function(x) range(x[,1])))
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112 ylim = range(sapply(TIC, function(x) range(x[,2])))
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113 ylim = c(-ylim[2], ylim[2])
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114
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115
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116 ##plot start
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117
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118 if (length(phenoDataClass)>2){
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119 for (k in 1:(length(phenoDataClass)-1)){
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120 for (l in (k+1):length(phenoDataClass)){
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121 #print(paste(phenoDataClass[k],"vs",phenoDataClass[l],sep=" "))
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122 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k]," vs ",phenoDataClass[l], sep=""), xlab = "Retention Time (min)", ylab = "BPC")
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123 colvect<-NULL
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124 for (j in 1:length(classnames[[k]])) {
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125 tic <- TIC[[classnames[[k]][j]]]
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126 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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127 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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128 colvect<-append(colvect,cols[classnames[[k]][j]])
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129 }
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130 for (j in 1:length(classnames[[l]])) {
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131 # i=class2names[j]
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132 tic <- TIC[[classnames[[l]][j]]]
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133 points(tic[,1]/60, -tic[,2], col = cols[classnames[[l]][j]], pch = pch[classnames[[l]][j]], type="l")
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134 colvect<-append(colvect,cols[classnames[[l]][j]])
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135 }
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136 legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)
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137 }
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138 }
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139 }#end if length >2
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140
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141 if (length(phenoDataClass)==2){
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142 k=1
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143 l=2
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144 colvect<-NULL
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145 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k],"vs",phenoDataClass[l], sep=""), xlab = "Retention Time (min)", ylab = "BPC")
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146
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147 for (j in 1:length(classnames[[k]])) {
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148
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149 tic <- TIC[[classnames[[k]][j]]]
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150 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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151 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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152 colvect<-append(colvect,cols[classnames[[k]][j]])
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153 }
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154 for (j in 1:length(classnames[[l]])) {
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155 # i=class2names[j]
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156 tic <- TIC[[classnames[[l]][j]]]
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157 points(tic[,1]/60, -tic[,2], col = cols[classnames[[l]][j]], pch = pch[classnames[[l]][j]], type="l")
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158 colvect<-append(colvect,cols[classnames[[l]][j]])
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159 }
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160 legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)
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161
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162 }#end length ==2
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163
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164 #case where only one class
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165 if (length(phenoDataClass)==1){
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166 k=1
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167 ylim = range(sapply(TIC, function(x) range(x[,2])))
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168 colvect<-NULL
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169 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k], sep=""), xlab = "Retention Time (min)", ylab = "BPC")
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170
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171 for (j in 1:length(classnames[[k]])) {
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172 tic <- TIC[[classnames[[k]][j]]]
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173 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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174 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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175 colvect<-append(colvect,cols[classnames[[k]][j]])
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176 }
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177
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178 legend("topright",paste(basename(files[c(classnames[[k]])])), col = colvect, lty = lty, pch = pch)
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179
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180 }#end length ==1
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181
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182 dev.off() #pdf(pdfname,w=16,h=10)
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183
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184 invisible(TIC)
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185 }
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186
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187
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188
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189 #@author Y. Guitton
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190 getTIC <- function(file,rtcor=NULL) {
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191 object <- xcmsRaw(file)
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192 cbind(if (is.null(rtcor)) object@scantime else rtcor, rawEIC(object,mzrange=range(object@env$mz))$intensity)
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193 }
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194
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195 ##
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196 ## overlay TIC from all files in current folder or from xcmsSet, create pdf
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197 ##
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198 #@author Y. Guitton
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199 getTICs <- function(xcmsSet=NULL,files=NULL, pdfname="TICs.pdf",rt=c("raw","corrected")) {
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200 cat("Creating TIC pdf...\n")
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201
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202 if (is.null(xcmsSet)) {
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203 filepattern <- c("[Cc][Dd][Ff]", "[Nn][Cc]", "([Mm][Zz])?[Xx][Mm][Ll]", "[Mm][Zz][Dd][Aa][Tt][Aa]", "[Mm][Zz][Mm][Ll]")
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204 filepattern <- paste(paste("\\.", filepattern, "$", sep = ""), collapse = "|")
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205 if (is.null(files))
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206 files <- getwd()
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207 info <- file.info(files)
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208 listed <- list.files(files[info$isdir], pattern = filepattern, recursive = TRUE, full.names = TRUE)
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209 files <- c(files[!info$isdir], listed)
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210 } else {
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211 files <- filepaths(xcmsSet)
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212 }
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213
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214 phenoDataClass<-as.vector(levels(xcmsSet@phenoData[,1])) #sometime phenoData have more than 1 column use first as class
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215 classnames<-vector("list",length(phenoDataClass))
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216 for (i in 1:length(phenoDataClass)){
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217 classnames[[i]]<-which( xcmsSet@phenoData[,1]==phenoDataClass[i])
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218 }
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219
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220 N <- length(files)
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221 TIC <- vector("list",N)
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222
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223 for (i in 1:N) {
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224 if (!is.null(xcmsSet) && rt == "corrected")
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225 rtcor <- xcmsSet@rt$corrected[[i]] else
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226 rtcor <- NULL
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227 TIC[[i]] <- getTIC(files[i],rtcor=rtcor)
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228 }
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229
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230 pdf(pdfname,w=16,h=10)
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231 cols <- rainbow(N)
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232 lty = 1:N
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233 pch = 1:N
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234 #search for max x and max y in TICs
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235 xlim = range(sapply(TIC, function(x) range(x[,1])))
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236 ylim = range(sapply(TIC, function(x) range(x[,2])))
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237 ylim = c(-ylim[2], ylim[2])
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238
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239
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240 ##plot start
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241 if (length(phenoDataClass)>2){
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242 for (k in 1:(length(phenoDataClass)-1)){
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243 for (l in (k+1):length(phenoDataClass)){
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244 #print(paste(phenoDataClass[k],"vs",phenoDataClass[l],sep=" "))
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245 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k]," vs ",phenoDataClass[l], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
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246 colvect<-NULL
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247 for (j in 1:length(classnames[[k]])) {
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248 tic <- TIC[[classnames[[k]][j]]]
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249 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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250 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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251 colvect<-append(colvect,cols[classnames[[k]][j]])
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252 }
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253 for (j in 1:length(classnames[[l]])) {
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254 # i=class2names[j]
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255 tic <- TIC[[classnames[[l]][j]]]
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256 points(tic[,1]/60, -tic[,2], col = cols[classnames[[l]][j]], pch = pch[classnames[[l]][j]], type="l")
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257 colvect<-append(colvect,cols[classnames[[l]][j]])
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258 }
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259 legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)
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260 }
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261 }
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262 }#end if length >2
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263 if (length(phenoDataClass)==2){
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264 k=1
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265 l=2
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266
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267 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k],"vs",phenoDataClass[l], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
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268 colvect<-NULL
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269 for (j in 1:length(classnames[[k]])) {
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270 tic <- TIC[[classnames[[k]][j]]]
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271 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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272 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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273 colvect<-append(colvect,cols[classnames[[k]][j]])
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274 }
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275 for (j in 1:length(classnames[[l]])) {
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276 # i=class2names[j]
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277 tic <- TIC[[classnames[[l]][j]]]
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278 points(tic[,1]/60, -tic[,2], col = cols[classnames[[l]][j]], pch = pch[classnames[[l]][j]], type="l")
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279 colvect<-append(colvect,cols[classnames[[l]][j]])
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280 }
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281 legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)
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282
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283 }#end length ==2
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284
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285 #case where only one class
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286 if (length(phenoDataClass)==1){
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287 k=1
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288 ylim = range(sapply(TIC, function(x) range(x[,2])))
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289
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290 plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
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291 colvect<-NULL
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292 for (j in 1:length(classnames[[k]])) {
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293 tic <- TIC[[classnames[[k]][j]]]
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294 # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
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295 points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
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296 colvect<-append(colvect,cols[classnames[[k]][j]])
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297 }
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298
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299 legend("topright",paste(basename(files[c(classnames[[k]])])), col = colvect, lty = lty, pch = pch)
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300
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301 }#end length ==1
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302
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303 dev.off() #pdf(pdfname,w=16,h=10)
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304
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305 invisible(TIC)
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306 }
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307
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308
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309
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310 ##
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311 ## Get the polarities from all the samples of a condition
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312 #@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM
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313 #@author Gildas Le Corguille lecorguille@sb-roscoff.fr ABiMS TEAM
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314 getSampleMetadata <- function(xcmsSet=NULL, sampleMetadataOutput="sampleMetadata.tsv") {
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315 cat("Creating the sampleMetadata file...\n")
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316
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317 #Create the sampleMetada dataframe
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318 sampleMetadata=xset@phenoData
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319 sampleNamesOrigin=rownames(sampleMetadata)
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320 sampleNamesMakeNames=make.names(sampleNamesOrigin)
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321
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322 if (any(duplicated(sampleNamesMakeNames))) {
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323 write("\n\nERROR: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names().\nIn your case, at least two columns after the renaming obtain the same name, thus XCMS will collapse those columns per name.", stderr())
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324 for (sampleName in sampleNamesOrigin) {
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325 write(paste(sampleName,"\t->\t",make.names(sampleName)),stderr())
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326 }
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327 stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.")
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328 }
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329
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330 if (!all(sampleNamesOrigin == sampleNamesMakeNames)) {
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331 cat("\n\nWARNING: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names()\nIn your case, one or more sample names will be renamed in the sampleMetadata and dataMatrix files:\n")
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332 for (sampleName in sampleNamesOrigin) {
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333 cat(paste(sampleName,"\t->\t",make.names(sampleName),"\n"))
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334 }
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335 }
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336
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337 sampleMetadata$sampleMetadata=sampleNamesMakeNames
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338 sampleMetadata=cbind(sampleMetadata["sampleMetadata"],sampleMetadata["class"]) #Reorder columns
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339 rownames(sampleMetadata)=NULL
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340
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341 #Create a list of files name in the current directory
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342 list_files=xset@filepaths
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343 #For each sample file, the following actions are done
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344 for (file in list_files){
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345 #Check if the file is in the CDF format
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346 if (!mzR:::netCDFIsFile(file)){
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347
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348 # If the column isn't exist, with add one filled with NA
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349 if (is.null(sampleMetadata$polarity)) sampleMetadata$polarity=NA
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350
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351 #Create a simple xcmsRaw object for each sample
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352 xcmsRaw=xcmsRaw(file)
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353 #Extract the polarity (a list of polarities)
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354 polarity=xcmsRaw@polarity
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355 #Verify if all the scans have the same polarity
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356 uniq_list=unique(polarity)
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357 if (length(uniq_list)>1){
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358 polarity="mixed"
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359 } else {
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360 polarity=as.character(uniq_list)
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361 }
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362 #Transforms the character to obtain only the sample name
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363 filename=basename(file)
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364 library(tools)
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365 samplename=file_path_sans_ext(filename)
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366
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367 #Set the polarity attribute
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368 sampleMetadata$polarity[sampleMetadata$sampleMetadata==samplename]=polarity
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369
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370 #Delete xcmsRaw object because it creates a bug for the fillpeaks step
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371 rm(xcmsRaw)
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372 }
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373
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374 }
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375
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376 write.table(sampleMetadata, sep="\t", quote=FALSE, row.names=FALSE, file=sampleMetadataOutput)
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377
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378 return(list("sampleNamesOrigin"=sampleNamesOrigin,"sampleNamesMakeNames"=sampleNamesMakeNames))
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379
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380 }
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381
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382
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383 ##
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384 ## This function check if xcms will found all the files
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385 ##
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386 #@author Gildas Le Corguille lecorguille@sb-roscoff.fr ABiMS TEAM
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387 checkFilesCompatibilityWithXcms <- function(directory) {
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388 cat("Checking files filenames compatibilities with xmcs...\n")
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389 # WHAT XCMS WILL FIND
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390 filepattern <- c("[Cc][Dd][Ff]", "[Nn][Cc]", "([Mm][Zz])?[Xx][Mm][Ll]","[Mm][Zz][Dd][Aa][Tt][Aa]", "[Mm][Zz][Mm][Ll]")
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391 filepattern <- paste(paste("\\.", filepattern, "$", sep = ""),collapse = "|")
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392 info <- file.info(directory)
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393 listed <- list.files(directory[info$isdir], pattern = filepattern,recursive = TRUE, full.names = TRUE)
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394 files <- c(directory[!info$isdir], listed)
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395 files_abs <- file.path(getwd(), files)
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396 exists <- file.exists(files_abs)
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397 files[exists] <- files_abs[exists]
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398 files[exists] <- sub("//","/",files[exists])
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399
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400 # WHAT IS ON THE FILESYSTEM
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401 filesystem_filepaths=system(paste("find $PWD/",directory," -not -name '\\.*' -not -path '*conda-env*' -type f -name \"*\"", sep=""), intern=T)
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402 filesystem_filepaths=filesystem_filepaths[grep(filepattern, filesystem_filepaths, perl=T)]
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403
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404 # COMPARISON
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405 if (!is.na(table(filesystem_filepaths %in% files)["FALSE"])) {
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406 write("\n\nERROR: List of the files which will not be imported by xcmsSet",stderr())
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407 write(filesystem_filepaths[!(filesystem_filepaths %in% files)],stderr())
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408 stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.")
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409 }
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410 }
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411
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412
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413
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414 ##
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415 ## This function check if XML contains special caracters. It also checks integrity and completness.
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416 ##
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417 #@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM
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418 checkXmlStructure <- function (directory) {
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419 cat("Checking XML structure...\n")
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420
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421 cmd=paste("IFS=$'\n'; for xml in $(find",directory,"-not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'); do if [ $(xmllint --nonet --noout \"$xml\" 2> /dev/null; echo $?) -gt 0 ]; then echo $xml;fi; done;")
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422 capture=system(cmd,intern=TRUE)
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423
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424 if (length(capture)>0){
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425 #message=paste("The following mzXML or mzML file is incorrect, please check these files first:",capture)
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426 write("\n\nERROR: The following mzXML or mzML file(s) are incorrect, please check these files first:", stderr())
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427 write(capture, stderr())
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428 stop("ERROR: xcmsSet cannot continue with incorrect mzXML or mzML files")
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429 }
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430
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431 }
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432
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433
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434 ##
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435 ## This function check if XML contain special characters
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436 ##
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437 #@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM
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438 deleteXmlBadCharacters<- function (directory) {
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439 cat("Checking Non ASCII characters in the XML...\n")
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440
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441 processed=F
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442 l=system( paste("find",directory, "-not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'"),intern=TRUE)
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443 for (i in l){
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444 cmd=paste("LC_ALL=C grep '[^ -~]' \"",i,"\"",sep="")
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445 capture=suppressWarnings(system(cmd,intern=TRUE))
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446 if (length(capture)>0){
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447 cmd=paste("perl -i -pe 's/[^[:ascii:]]//g;'",i)
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448 print( paste("WARNING: Non ASCII characters have been removed from the ",i,"file") )
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449 c=system(cmd,intern=TRUE)
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450 capture=""
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451 processed=T
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452 }
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453 }
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454 if (processed) cat("\n\n")
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455 return(processed)
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456 }
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457
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458
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459 ##
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460 ## This function will compute MD5 checksum to check the data integrity
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461 ##
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462 #@author Gildas Le Corguille lecorguille@sb-roscoff.fr
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463 getMd5sum <- function (directory) {
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464 cat("Compute md5 checksum...\n")
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465 # WHAT XCMS WILL FIND
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466 filepattern <- c("[Cc][Dd][Ff]", "[Nn][Cc]", "([Mm][Zz])?[Xx][Mm][Ll]","[Mm][Zz][Dd][Aa][Tt][Aa]", "[Mm][Zz][Mm][Ll]")
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467 filepattern <- paste(paste("\\.", filepattern, "$", sep = ""),collapse = "|")
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468 info <- file.info(directory)
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469 listed <- list.files(directory[info$isdir], pattern = filepattern,recursive = TRUE, full.names = TRUE)
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470 files <- c(directory[!info$isdir], listed)
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471 exists <- file.exists(files)
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472 files <- files[exists]
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473
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474 library(tools)
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475
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476 #cat("\n\n")
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477
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478 return(as.matrix(md5sum(files)))
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479 }