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1 #options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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2
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3 args <- commandArgs(trailingOnly = TRUE)
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4
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5 inFile = args[1]
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6 outFile = args[2]
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7 outDir = args[3]
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8 clonalType = args[4]
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9 species = args[5]
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10
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11 if (!("gridExtra" %in% rownames(installed.packages()))) {
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12 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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13 }
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14 library(gridExtra)
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15 if (!("ggplot2" %in% rownames(installed.packages()))) {
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16 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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17 }
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18 require(ggplot2)
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19 if (!("plyr" %in% rownames(installed.packages()))) {
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20 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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21 }
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22 require(plyr)
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23
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24 if (!("data.table" %in% rownames(installed.packages()))) {
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25 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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26 }
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27 library(data.table)
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28
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29
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30 test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="")
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31
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32 test = test[test$Sample != "",]
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33
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34 test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene)
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35 test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene)
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36 test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene)
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37
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38 #test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":"))
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39 test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":"))
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40
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41 PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ]
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42 if("Functionality" %in% colnames(test)) {
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43 PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ]
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44 }
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45
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46 NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ]
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47
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48 #PRODF = PROD[ -1]
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49
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50 PRODF = PROD
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51
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52 #PRODF = unique(PRODF)
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53 PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ]
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54
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55 PRODFV = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.V.Gene")])
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56 PRODFV$Length = as.numeric(PRODFV$Length)
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57 Total = 0
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58 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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59 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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60 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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61
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62 PRODFD = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.D.Gene")])
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63 PRODFD$Length = as.numeric(PRODFD$Length)
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64 Total = 0
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65 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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66 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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67 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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68
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69 PRODFJ = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.J.Gene")])
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70 PRODFJ$Length = as.numeric(PRODFJ$Length)
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71 Total = 0
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72 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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73 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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74 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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75
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76 V = c("v.name\tchr.orderV\nTRBV1\t1\nTRBV2\t2\nTRBV3\t3\nTRBV4\t4\nTRBV5\t5\nTRBV12-1\t6\nTRBV13-1\t7\nTRBV12-2\t8\nTRBV13-2\t9\nTRBV13-3\t10\nTRBV14\t11\nTRBV15\t12\nTRBV16\t13\nTRBV17\t14\nTRBV19\t15\nTRBV20\t16\nTRBV23\t17\nTRBV24\t18\nTRBV26\t19\nTRBV29\t20\nTRBV30\t21\nTRBV31\t22\n")
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77 tcV = textConnection(V)
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78 Vchain = read.table(tcV, sep="\t", header=TRUE)
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79 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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80 close(tcV)
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81
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82 D = c("v.name\tchr.orderD\nTRBD1\t1\nTRBD2\t2\n")
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83 tcD = textConnection(D)
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84 Dchain = read.table(tcD, sep="\t", header=TRUE)
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85 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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86 close(tcD)
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87
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88
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89 J = c("v.name\tchr.orderJ\nTRBJ1-1\t1\nTRBJ1-2\t2\nTRBJ1-3\t3\nTRBJ1-4\t4\nTRBJ1-5\t5\nTRBJ2-1\t6\nTRBJ2-2\t7\nTRBJ2-3\t8\nTRBJ2-4\t9\nTRBJ2-5\t10\nTRBJ2-6\t11\nTRBJ2-7\t12\n")
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90 tcJ = textConnection(J)
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91 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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92 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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93 close(tcJ)
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94
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95 setwd(outDir)
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96
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97 write.table(PRODF, "allUnique.tsv", sep="\t",quote=F,row.names=F,col.names=T)
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98
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99 pV = ggplot(PRODFV)
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100 pV = pV + geom_bar( aes( x=factor(reorder(Top.V.Gene, chr.orderV)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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101 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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102
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103 png("VPlot.png",width = 1280, height = 720)
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104 pV
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105 dev.off();
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106
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107 pD = ggplot(PRODFD)
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108 pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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109 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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110
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111 png("DPlot.png",width = 800, height = 600)
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112 pD
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113 dev.off();
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114
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115 pJ = ggplot(PRODFJ)
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116 pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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117 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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118
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119 png("JPlot.png",width = 800, height = 600)
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120 pJ
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121 dev.off();
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122
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123 revVchain = Vchain
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124 revDchain = Dchain
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125 revVchain$chr.orderV = rev(revVchain$chr.orderV)
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126 revDchain$chr.orderD = rev(revDchain$chr.orderD)
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127
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128 cat("before VD", "\n")
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129
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130 plotVD <- function(dat){
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131 if(length(dat[,1]) == 0){
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132 return()
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133 }
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134 img = ggplot() +
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135 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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136 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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137 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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138 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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139 xlab("D genes") +
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140 ylab("V Genes")
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141
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142 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
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143 print(img)
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144 dev.off()
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145 }
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146
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147 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
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148
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149 VandDCount$l = log(VandDCount$Length)
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150 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
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151 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
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152 VandDCount$relLength = VandDCount$l / VandDCount$max
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153
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154 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(test$Sample))
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155
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156 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
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157 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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158 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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159 VDList = split(completeVD, f=completeVD[,"Sample"])
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160
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161 lapply(VDList, FUN=plotVD)
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162
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163 cat("after VD", "\n")
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164
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165 cat("before VJ", "\n")
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166
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167 plotVJ <- function(dat){
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168 if(length(dat[,1]) == 0){
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169 return()
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170 }
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171 img = ggplot() +
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172 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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173 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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174 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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175 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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176 xlab("J genes") +
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177 ylab("V Genes")
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178
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179 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
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180 print(img)
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181 dev.off()
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182 }
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183
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184 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
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185
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186 VandJCount$l = log(VandJCount$Length)
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187 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
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188 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
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189 VandJCount$relLength = VandJCount$l / VandJCount$max
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190
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191 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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192
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193 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
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194 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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195 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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196 VJList = split(completeVJ, f=completeVJ[,"Sample"])
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197 lapply(VJList, FUN=plotVJ)
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198
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199 cat("after VJ", "\n")
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200
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201 cat("before DJ", "\n")
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202
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203 plotDJ <- function(dat){
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204 if(length(dat[,1]) == 0){
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205 return()
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206 }
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207 img = ggplot() +
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208 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
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209 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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210 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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211 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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212 xlab("J genes") +
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213 ylab("D Genes")
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214
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215 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
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216 print(img)
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217 dev.off()
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218 }
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219
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220 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
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221
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222 DandJCount$l = log(DandJCount$Length)
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223 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
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224 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
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225 DandJCount$relLength = DandJCount$l / DandJCount$max
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226
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227 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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228
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229 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
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230 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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231 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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232 DJList = split(completeDJ, f=completeDJ[,"Sample"])
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233 lapply(DJList, FUN=plotDJ)
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234
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235 cat("after DJ", "\n")
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236
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237 sampleFile <- file("samples.txt")
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238 un = unique(test$Sample)
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239 un = paste(un, sep="\n")
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240 writeLines(un, sampleFile)
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241 close(sampleFile)
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242
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243
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244
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245 if("Replicate" %in% colnames(test))
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246 {
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247 clonalityFrame = PROD
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248 clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":"))
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249 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ]
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250 write.table(clonalityFrame, "clonalityComplete.tsv", sep="\t",quote=F,row.names=F,col.names=T)
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251
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252 ClonalitySampleReplicatePrint <- function(dat){
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253 write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".tsv", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
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254 }
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255
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256 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
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257 lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
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258
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259 ClonalitySamplePrint <- function(dat){
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260 write.table(dat, paste("clonality_", unique(dat$Sample) , ".tsv", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
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261 }
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262
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263 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
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264 lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
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265
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266 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "VDJCDR3")])
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267 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
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268 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
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269 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
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270 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
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271
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272 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
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273 tcct = textConnection(ct)
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274 CT = read.table(tcct, sep="\t", header=TRUE)
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275 close(tcct)
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276 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
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277 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
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278
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279 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "VDJCDR3")])
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280 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
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281 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
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282 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
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283
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284 ReplicatePrint <- function(dat){
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285 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
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286 }
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287
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288 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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289 lapply(ReplicateSplit, FUN=ReplicatePrint)
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290
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291 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")])
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292 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
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293
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294
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295 ReplicateSumPrint <- function(dat){
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296 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
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297 }
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298
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299 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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300 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
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301
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302 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
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303 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
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304 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
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305 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
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306 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
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307
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308 ClonalityScorePrint <- function(dat){
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309 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
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310 }
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311
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312 clonalityScore = clonalFreqCount[c("Sample", "Result")]
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313 clonalityScore = unique(clonalityScore)
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314
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315 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
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316 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
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317
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318 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
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319
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320
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321
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322 ClonalityOverviewPrint <- function(dat){
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323 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
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324 }
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325
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326 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
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327 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
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328 }
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329
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330 if("Functionality" %in% colnames(test))
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331 {
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332 newData = data.frame(data.table(PROD)[,list(unique=.N,
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333 VH.DEL=mean(X3V.REGION.trimmed.nt.nb),
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334 P1=mean(P3V.nt.nb),
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335 N1=mean(N1.REGION.nt.nb),
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336 P2=mean(P5D.nt.nb),
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337 DEL.DH=mean(X5D.REGION.trimmed.nt.nb),
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338 DH.DEL=mean(X3D.REGION.trimmed.nt.nb),
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339 P3=mean(P3D.nt.nb),
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340 N2=mean(N2.REGION.nt.nb),
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341 P4=mean(P5J.nt.nb),
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342 DEL.JH=mean(X5J.REGION.trimmed.nt.nb),
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343 Total.Del=( mean(X3V.REGION.trimmed.nt.nb) +
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344 mean(X5D.REGION.trimmed.nt.nb) +
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345 mean(X3D.REGION.trimmed.nt.nb) +
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346 mean(X5J.REGION.trimmed.nt.nb)),
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347
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348 Total.N=( mean(N1.REGION.nt.nb) +
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349 mean(N2.REGION.nt.nb)),
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350
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351 Total.P=( mean(P3V.nt.nb) +
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352 mean(P5D.nt.nb) +
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353 mean(P3D.nt.nb) +
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354 mean(P5J.nt.nb))),
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355 by=c("Sample")])
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356 write.table(newData, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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357 }
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