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planemo upload for repository https://github.com/workflow4metabolomics/anova commit 493595bdd63ff88e7b93f22d8a092a70d4f39a05
author lecorguille
date Mon, 05 Mar 2018 09:24:51 -0500
parents 8190dfb5a351
children
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1 #!/usr/local/public/bin/Rscript
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2 # version="1.1"
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4 # date: 06-06-2012
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5 # update: 18-02-2014
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6 # **Authors** Gildas Le Corguille ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr
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8 # abims_anova.r version 20140218
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10 library(batch)
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13 # function avova
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14 anova = function (file, sampleinfo, varinfo, mode="column", condition=1, interaction=F, method="BH", threshold=0.01, selection_method="intersection", sep=";", dec=".", outputdatapvalue="anova.data.output", outputdatasignif="anova.datasignif.output") {
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17 if (sep=="tabulation") sep="\t"
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18 if (sep=="semicolon") sep=";"
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19 if (sep=="comma") sep=","
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21 anova_formula_operator = "+"
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22 if (interaction) anova_formula_operator = "*"
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24 # -- import --
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25 data=read.table(file, header = TRUE, row.names=1, sep = sep, quote="\"", dec = dec, fill = TRUE, comment.char="",na.strings = "NA", check.names=FALSE)
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27 if (mode == "row") {data=t(data)} else {data=as.matrix(data)}
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29 sampleinfoTab=read.table(sampleinfo, header = TRUE, row.names=1, sep = sep, quote="\"")
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30 rownames(sampleinfoTab) = make.names(rownames(sampleinfoTab))
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31
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32 varinfoTab=read.table(varinfo, header = TRUE, sep = sep, quote="\"")
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33 if(sum(colnames(data)!=varinfoTab[,1])!=0){ # if ID not exactly identical
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34 if(sum(colnames(data)[order(colnames(data))]!=varinfoTab[order(varinfoTab[,1]),1])==0){
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35 # reorder data matrix to match variable metadata order
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36 data = data[,match(varinfoTab[,1],colnames(data))]
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37 }else{
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38 stop(paste0("\nVariables' ID do not match between your data matrix and your variable",
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39 "metadata file. \nPlease check your data."))
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40 }
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41 }
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42
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43 # -- group --
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44 match_data_sampleinfoTab = match(rownames(data),rownames(sampleinfoTab))
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45 if (sum(is.na(match_data_sampleinfoTab)) > 0) {
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46 write("ERROR: There is a problem during to match sample names from the data matrix and from the sample info (presence of NA).", stderr())
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47 write("You may need to use change the mode (column/row)", stderr())
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48 write("10 first sample names in the data matrix:", stderr())
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49 write(head(colnames(data)), stderr())
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50 write("10 first sample names in the sample info:", stderr())
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51 write(head(rownames(sampleinfoTab)), stderr())
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52 quit("no",status=10)
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53 }
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56 # -- anova --
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58 # formula
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59 grps=list()
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60 anova_formula_s = "data ~ "
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61 cat("\ncontrasts:\n")
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62 for (i in 1:length(condition)) {
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63 grps[[i]] = factor(sampleinfoTab[,condition[i]][match_data_sampleinfoTab])
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64 anova_formula_s = paste(anova_formula_s, "grps[[",i,"]]",anova_formula_operator, sep="")
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65 cat(condition[i],"\t",levels(grps[[i]]),"\n")
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66 # write("Current groups: ", stderr())
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67 # write(grp[[i]], stderr())
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68 }
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69 anova_formula_s = substr(anova_formula_s, 1, nchar(anova_formula_s)-1)
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70 anova_formula = as.formula(anova_formula_s)
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72
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74 # anova
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75 manovaObjectList = manova(anova_formula)
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76 manovaList = summary.aov(manovaObjectList)
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78 # condition renaming
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79 manovaRownames = gsub(" ","",rownames(manovaList[[1]]))
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80 manovaNbrPvalue = length(manovaRownames)-1
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81 manovaRownames = manovaRownames[-(manovaNbrPvalue+1)]
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83 for (i in 1:length(condition)) {
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84 manovaRownames = sub(paste("grps\\[\\[",i,"\\]\\]",sep=""),condition[i],manovaRownames)
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85 anova_formula_s = sub(paste("grps\\[\\[",i,"\\]\\]",sep=""),condition[i],anova_formula_s)
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86 }
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87
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88 # log
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89 cat("\nanova_formula",anova_formula_s,"\n")
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91 # p-value
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92 aovPValue = sapply(manovaList,function(x){x[-(manovaNbrPvalue+1),5]})
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93 if(length(condition) == 1) aovPValue = t(aovPValue)
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94 rownames(aovPValue) = paste("pvalue_",manovaRownames,sep="")
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96 # p-value adjusted
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97 if(length(condition) == 1) {
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98 aovAdjPValue = t(p.adjust(aovPValue,method=method))
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99 } else {
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100 aovAdjPValue = t(apply(aovPValue,1,p.adjust, method=method))
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101 }
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102 rownames(aovAdjPValue) = paste("pval.",method,".",manovaRownames,sep="")
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103
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104 # selection
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105 colSumThreshold = colSums(aovAdjPValue <= threshold)
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106 if (selection_method == "intersection") {
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107 datafiltered = data[,colSumThreshold == nrow(aovAdjPValue ), drop=FALSE]
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108 } else {
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109 datafiltered = data[,colSumThreshold != 0, drop=FALSE]
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110 }
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111 selected.var = rep("no",ncol(data))
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112 selected.var[colnames(data)%in%colnames(datafiltered)] = "yes"
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113
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114 #data=rbind(data, aovPValue, aovAdjPValue)
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115 varinfoTab=cbind(varinfoTab, round(t(aovAdjPValue),10), selected.var)
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116
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117 # group means
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118 for (i in 1:length(condition)) {
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119 for(j in levels(grps[[i]])){
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120 subgp = rownames(sampleinfoTab[which(sampleinfoTab[,condition[i]]==j),])
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121 modmean = colMeans(data[which(rownames(data)%in%subgp),],na.rm=TRUE)
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122 varinfoTab=cbind(varinfoTab, modmean)
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123 colnames(varinfoTab)[ncol(varinfoTab)] = paste0("Mean_",condition[i],".",j)
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124 }
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125 }
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126 colnames(varinfoTab) = make.unique(colnames(varinfoTab))
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127
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128 # pdf for significant variables
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129 pdf(outputdatasignif)
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130 ### Venn diagram
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131 if(nrow(aovAdjPValue)>5){
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132 pie(100,labels=NA,main=paste0("Venn diagram only available for maximum 5 terms\n",
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133 "(your analysis concerns ",nrow(aovAdjPValue)," terms)\n\n",
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134 "Number of significant variables relatively to\nyour chosen threshold and ",
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135 "selection method: ",ncol(datafiltered)),cex.main=0.8)
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136 }else{
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137 vennlist = list(NULL)
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138 names(vennlist) = rownames(aovAdjPValue)[1]
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139 if(length(colnames(aovAdjPValue))==0){colnames(aovAdjPValue)=varinfoTab[,1]}
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140 for(i in 1:nrow(aovAdjPValue)){
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141 vennlist[[rownames(aovAdjPValue)[i]]]=colnames(aovAdjPValue[i,which(aovAdjPValue[i,]<=threshold),drop=FALSE])
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142 }
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143 if(length(vennlist)==0){ pie(100,labels=NA,main="No significant ions was found.")
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144 }else{ library(venn) ; venn(vennlist, zcolor="style", cexil=2, cexsn=1.5) }
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145 }
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146 ### Boxplot
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147 par(mfrow=c(2,2),mai=rep(0.5,4))
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148 data <- as.data.frame(data)
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149 for(i in 1:nrow(aovAdjPValue)){
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150 factmain = gsub(paste0("pval.",method,"."),"",rownames(aovAdjPValue)[i])
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151 factsignif = aovAdjPValue[i,which(aovAdjPValue[i,]<=threshold),drop=FALSE]
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152 if((ncol(factsignif)!=0)&(factmain%in%colnames(sampleinfoTab))){
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153 for(j in 1:ncol(factsignif)){
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154 varsignif = gsub(" Response ","",colnames(factsignif)[j])
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155 boxplot(as.formula(paste0("data$",varsignif," ~ sampleinfoTab$",factmain)),
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156 main=paste0(factmain,"\n",varsignif), col="grey", mai=7)
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157 }
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158 }
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159 }
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160 dev.off()
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161
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162 # summary for significant variables
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163 cat("\n\n- - - - - - - number of significant variables - - - - - - -\n\n")
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164 for(i in 1:nrow(aovAdjPValue)){
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165 cat(rownames(aovAdjPValue)[i],"-",
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166 sum(aovAdjPValue[i,]<=threshold),"significant variable(s)\n")
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167 }
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168 cat("\nIf some of your factors are missing here, this may be due to\neffects",
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169 "not estimable; your design may not be balanced enough.\n")
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170
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171 # -- output / return --
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172 write.table(varinfoTab, outputdatapvalue, sep=sep, quote=F, row.names=FALSE)
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173
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174 # log
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175 cat("\nthreshold:",threshold,"\n")
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176 cat("result:",ncol(datafiltered),"/",ncol(data),"\n\n")
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177
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178 quit("no",status=0)
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179 }
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180
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181 # log
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182 cat("ANOVA\n\n")
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183 cat("Arguments\n")
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184 args <- commandArgs(trailingOnly = TRUE)
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185 print(args)
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186
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187 listArguments = parseCommandArgs(evaluate=FALSE)
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188 do.call(anova, listArguments)