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0
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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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32
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7 outDir = args[3]
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0
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8
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32
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9 if (!require("gridExtra")) {
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35
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10 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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32
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11 }
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12 library(gridExtra)
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13 if (!require("ggplot2")) {
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35
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14 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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32
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15 }
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23
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16 require(ggplot2)
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32
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17 if (!require("plyr")) {
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35
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18 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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32
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19 }
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23
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20 require(plyr)
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0
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21
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22 test = read.table(inFile, sep="\t", header=TRUE)
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23
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24 test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene)
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25 test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene)
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26 test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene)
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27
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28 test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":"))
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29
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30 PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ]
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31
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32 NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ]
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33
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35
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34 PRODF = PROD[ -1]
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35
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32
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36 #PRODF = unique(PRODF)
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37 PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ]
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38
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35
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39
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40 uniqueCount = split(PRODF, f=PRODF[,"Sample"])
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41
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42 for(i in 1:length(uniqueCount)) {
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43 dat = data.frame(uniqueCount[i])
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44 sample = paste(unique(dat[,15]))
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45 uniqueCount[sample] = length(dat[,1])
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46 }
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47
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48 PRODFV = ddply(PRODF, c("Sample", "Top.V.Gene"), function(x) summary(x$VDJCDR3))
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49 PRODFV$Length = as.numeric(PRODFV$Length)
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50 Total = 0
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51 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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52 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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53 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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54
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35
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55 PRODFD = ddply(PRODF, c("Sample", "Top.D.Gene"), function(x) summary(x$VDJCDR3))
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56 PRODFD$Length = as.numeric(PRODFD$Length)
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57 Total = 0
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58 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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59 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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60 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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61
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35
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62 PRODFJ = ddply(PRODF, c("Sample", "Top.J.Gene"), function(x) summary(x$VDJCDR3))
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63 PRODFJ$Length = as.numeric(PRODFJ$Length)
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64 Total = 0
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65 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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66 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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67 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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68
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69 V = c("v.name\tchr.orderV\nIGHV7-81\t1\nIGHV3-74\t2\nIGHV3-73\t3\nIGHV3-72\t4\nIGHV3-71\t5\nIGHV2-70\t6\nIGHV1-69\t7\nIGHV3-66\t8\nIGHV3-64\t9\nIGHV4-61\t10\nIGHV4-59\t11\nIGHV1-58\t12\nIGHV3-53\t13\nIGHV3-52\t14\nIGHV5-a\t15\nIGHV5-51\t16\nIGHV3-49\t17\nIGHV3-48\t18\nIGHV3-47\t19\nIGHV1-46\t20\nIGHV1-45\t21\nIGHV3-43\t22\nIGHV4-39\t23\nIGHV3-35\t24\nIGHV4-34\t25\nIGHV3-33\t26\nIGHV4-31\t27\nIGHV4-30-4\t28\nIGHV4-30-2\t29\nIGHV3-30-3\t30\nIGHV3-30\t31\nIGHV4-28\t32\nIGHV2-26\t33\nIGHV1-24\t34\nIGHV3-23\t35\nIGHV3-22\t36\nIGHV3-21\t37\nIGHV3-20\t38\nIGHV3-19\t39\nIGHV1-18\t40\nIGHV3-15\t41\nIGHV3-13\t42\nIGHV3-11\t43\nIGHV3-9\t44\nIGHV1-8\t45\nIGHV3-7\t46\nIGHV2-5\t47\nIGHV7-4-1\t48\nIGHV4-4\t49\nIGHV4-b\t50\nIGHV1-3\t51\nIGHV1-2\t52\nIGHV6-1\t53")
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70 tcV = textConnection(V)
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71 Vchain = read.table(tcV, sep="\t", header=TRUE)
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72 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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73 close(tcV)
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74
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75 D = c("v.name\tchr.orderD\nIGHD1-1\t1\nIGHD2-2\t2\nIGHD3-3\t3\nIGHD6-6\t4\nIGHD1-7\t5\nIGHD2-8\t6\nIGHD3-9\t7\nIGHD3-10\t8\nIGHD4-11\t9\nIGHD5-12\t10\nIGHD6-13\t11\nIGHD1-14\t12\nIGHD2-15\t13\nIGHD3-16\t14\nIGHD4-17\t15\nIGHD5-18\t16\nIGHD6-19\t17\nIGHD1-20\t18\nIGHD2-21\t19\nIGHD3-22\t20\nIGHD4-23\t21\nIGHD5-24\t22\nIGHD6-25\t23\nIGHD1-26\t24\nIGHD7-27\t25")
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76 tcD = textConnection(D)
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77 Dchain = read.table(tcD, sep="\t", header=TRUE)
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78 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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79 close(tcD)
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80
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81
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82 J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6")
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83 tcJ = textConnection(J)
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84 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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85 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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86 close(tcJ)
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87
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32
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88 setwd(outDir)
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89
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0
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90 pV = ggplot(PRODFV)
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91 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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92 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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32
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93
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94 png("VPlot.png",width = 1280, height = 720)
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95 pV
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96 dev.off();
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97
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98 pD = ggplot(PRODFD)
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99 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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100 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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32
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101
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102 png("DPlot.png",width = 800, height = 600)
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103 pD
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104 dev.off();
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105
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106 pJ = ggplot(PRODFJ)
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107 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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108 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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109
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110 png("JPlot.png",width = 800, height = 600)
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111 pJ
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112 dev.off();
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113
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0
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114
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32
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115 plotVD <- function(dat){
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116 img = ggplot() +
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117 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=log)) +
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118 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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35
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119 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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120 ggtitle(paste(unique(dat$Sample), " (N=" , uniqueCount[paste(unique(dat$Sample))] ,")", sep="")) +
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32
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121 xlab("D genes") +
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122 ylab("V Genes")
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123
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124 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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125 print(img)
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126 dev.off()
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127 }
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128
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129
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35
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130 VandDCount = ddply(PRODF, c("Top.V.Gene", "Top.D.Gene", "Sample"), function(x) summary(x$VDJCDR3))
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32
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131 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(test$Sample))
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132
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133 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
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134 completeVD$Length = as.numeric(completeVD$Length)
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135 completeVD$log = log(completeVD$Length)
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136 completeVD = merge(completeVD, Vchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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137 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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138 #completeVD$log[is.na(completeVD$log)] = 0
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139 l = split(completeVD, f=completeVD[,"Sample"])
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140
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141 lapply(l, FUN=plotVD)
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142
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0
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143
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29
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144
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32
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145 plotVJ <- function(dat){
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146 img = ggplot() +
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147 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=log)) +
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148 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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149 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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35
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150 ggtitle(paste(unique(dat$Sample), " (N=" , uniqueCount[paste(unique(dat$Sample))] ,")", sep="")) +
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32
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151 xlab("J genes") +
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152 ylab("V Genes")
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153
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154 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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155 print(img)
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156 dev.off()
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157 }
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158
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159 VandJCount = ddply(PRODF, c("Top.V.Gene", "Top.J.Gene", "Sample"), function(x) summary(x$VDJCDR3))
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32
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160 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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161
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162 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
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163 completeVJ$Length = as.numeric(completeVJ$Length)
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164 completeVJ$log = log(completeVJ$Length)
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165 completeVJ = merge(completeVJ, Vchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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166 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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167 #completeVJ$log[is.na(completeVJ$log)] = 0
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168 l = split(completeVJ, f=completeVJ[,"Sample"])
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169 lapply(l, FUN=plotVJ)
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170
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171 plotDJ <- function(dat){
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172 img = ggplot() +
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173 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=log)) +
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174 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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175 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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176 ggtitle(paste(unique(dat$Sample), " (N=" , uniqueCount[paste(unique(dat$Sample))] ,")", sep="")) +
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177 xlab("J genes") +
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178 ylab("D Genes")
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179
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180 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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181 print(img)
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182 dev.off()
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183 }
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184
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35
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185 DandJCount = ddply(PRODF, c("Top.D.Gene", "Top.J.Gene", "Sample"), function(x) summary(x$VDJCDR3))
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32
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186 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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187
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188 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
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189 completeDJ$Length = as.numeric(completeDJ$Length)
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190 completeDJ$log = log(completeDJ$Length)
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191 completeDJ = merge(completeDJ, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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192 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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193 #completeDJ$log[is.na(completeDJ$log)] = 0
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194 l = split(completeDJ, f=completeDJ[,"Sample"])
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195 lapply(l, FUN=plotDJ)
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0
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196
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197
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32
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198 sampleFile <- file("samples.txt")
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199 un = unique(test$Sample)
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200 un = paste(un, sep="\n")
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201 writeLines(un, sampleFile)
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202 close(sampleFile)
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