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47
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1 #options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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0
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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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42
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8 clonalType = args[4]
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0
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9
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36
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10 if (!("gridExtra" %in% rownames(installed.packages()))) {
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11 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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32
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12 }
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13 library(gridExtra)
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36
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14 if (!("ggplot2" %in% rownames(installed.packages()))) {
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15 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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32
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16 }
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23
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17 require(ggplot2)
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36
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18 if (!("plyr" %in% rownames(installed.packages()))) {
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19 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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32
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20 }
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23
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21 require(plyr)
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0
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22
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36
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23 if (!("data.table" %in% rownames(installed.packages()))) {
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24 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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25 }
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26 library(data.table)
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27
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28
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52
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29 test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="")
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0
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30
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39
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31 test = test[test$Sample != "",]
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32
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0
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33 test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene)
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34 test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene)
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35 test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene)
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36
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42
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37 #test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":"))
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38 test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":"))
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0
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39
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40 PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ]
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51
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41 if("Functionality" %in% colnames(test)) {
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42 PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ]
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43 }
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0
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44
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45 NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ]
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46
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36
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47 #PRODF = PROD[ -1]
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48
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49 PRODF = PROD
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0
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50
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32
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51 #PRODF = unique(PRODF)
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52 PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ]
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53
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36
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54 PRODFV = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.V.Gene")])
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0
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55 PRODFV$Length = as.numeric(PRODFV$Length)
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56 Total = 0
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57 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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58 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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59 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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60
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36
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61 PRODFD = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.D.Gene")])
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0
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62 PRODFD$Length = as.numeric(PRODFD$Length)
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63 Total = 0
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64 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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65 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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66 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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67
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36
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68 PRODFJ = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.J.Gene")])
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0
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69 PRODFJ$Length = as.numeric(PRODFJ$Length)
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70 Total = 0
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71 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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72 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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73 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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74
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47
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75 V = c("v.name\tchr.orderV\nIGHV7-81\t1\nIGHV3-74\t2\nIGHV3-73\t3\nIGHV3-72\t4\nIGHV2-70\t6\nIGHV1-69\t7\nIGHV3-66\t8\nIGHV3-64\t9\nIGHV4-61\t10\nIGHV4-59\t11\nIGHV1-58\t12\nIGHV3-53\t13\nIGHV5-a\t15\nIGHV5-51\t16\nIGHV3-49\t17\nIGHV3-48\t18\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\nIGHV3-21\t37\nIGHV3-20\t38\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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0
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76 tcV = textConnection(V)
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77 Vchain = read.table(tcV, sep="\t", header=TRUE)
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78 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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79 close(tcV)
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80
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81 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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82 tcD = textConnection(D)
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83 Dchain = read.table(tcD, sep="\t", header=TRUE)
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84 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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85 close(tcD)
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86
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87
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88 J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6")
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89 tcJ = textConnection(J)
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90 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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91 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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92 close(tcJ)
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93
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32
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94 setwd(outDir)
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95
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51
|
96 write.table(PRODF, "allUnique.tsv", sep="\t",quote=F,row.names=F,col.names=T)
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49
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97
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0
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98 pV = ggplot(PRODFV)
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99 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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29
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100 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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101
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102 png("VPlot.png",width = 1280, height = 720)
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103 pV
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104 dev.off();
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0
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105
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106 pD = ggplot(PRODFD)
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107 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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29
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108 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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109
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110 png("DPlot.png",width = 800, height = 600)
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111 pD
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112 dev.off();
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0
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113
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114 pJ = ggplot(PRODFJ)
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115 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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32
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116 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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117
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118 png("JPlot.png",width = 800, height = 600)
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119 pJ
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120 dev.off();
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121
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38
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122 revVchain = Vchain
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123 revDchain = Dchain
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124 revVchain$chr.orderV = rev(revVchain$chr.orderV)
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125 revDchain$chr.orderD = rev(revDchain$chr.orderD)
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0
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126
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32
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127 plotVD <- function(dat){
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39
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128 if(length(dat[,1]) == 0){
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129 return()
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130 }
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32
|
131 img = ggplot() +
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38
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132 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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32
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133 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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47
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134 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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38
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135 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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32
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136 xlab("D genes") +
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137 ylab("V Genes")
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138
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139 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
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140 print(img)
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141 dev.off()
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142 }
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143
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36
|
144 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
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38
|
145
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146 VandDCount$l = log(VandDCount$Length)
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147 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
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148 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
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149 VandDCount$relLength = VandDCount$l / VandDCount$max
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150
|
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32
|
151 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(test$Sample))
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152
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153 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
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38
|
154 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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32
|
155 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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38
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156 VDList = split(completeVD, f=completeVD[,"Sample"])
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32
|
157
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38
|
158 lapply(VDList, FUN=plotVD)
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|
32
|
159
|
|
0
|
160
|
|
29
|
161
|
|
32
|
162 plotVJ <- function(dat){
|
|
39
|
163 if(length(dat[,1]) == 0){
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164 return()
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165 }
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|
32
|
166 img = ggplot() +
|
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38
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167 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
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32
|
168 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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47
|
169 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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38
|
170 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
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32
|
171 xlab("J genes") +
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172 ylab("V Genes")
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173
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174 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
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175 print(img)
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176 dev.off()
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|
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177 }
|
|
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178
|
|
36
|
179 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
38
|
180
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|
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181 VandJCount$l = log(VandJCount$Length)
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|
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182 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
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|
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183 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
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|
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184 VandJCount$relLength = VandJCount$l / VandJCount$max
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|
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185
|
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32
|
186 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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|
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187
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|
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188 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
38
|
189 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
32
|
190 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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|
38
|
191 VJList = split(completeVJ, f=completeVJ[,"Sample"])
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|
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192 lapply(VJList, FUN=plotVJ)
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|
32
|
193
|
|
|
194 plotDJ <- function(dat){
|
|
39
|
195 if(length(dat[,1]) == 0){
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|
|
196 return()
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|
|
197 }
|
|
32
|
198 img = ggplot() +
|
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38
|
199 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
32
|
200 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
47
|
201 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
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38
|
202 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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|
32
|
203 xlab("J genes") +
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204 ylab("D Genes")
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205
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206 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
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|
|
207 print(img)
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|
|
208 dev.off()
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|
|
209 }
|
|
|
210
|
|
36
|
211 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
38
|
212
|
|
|
213 DandJCount$l = log(DandJCount$Length)
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|
|
214 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
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|
|
215 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
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|
|
216 DandJCount$relLength = DandJCount$l / DandJCount$max
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|
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217
|
|
32
|
218 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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|
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219
|
|
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220 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
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|
38
|
221 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
32
|
222 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
38
|
223 DJList = split(completeDJ, f=completeDJ[,"Sample"])
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|
|
224 lapply(DJList, FUN=plotDJ)
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|
0
|
225
|
|
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226
|
|
32
|
227 sampleFile <- file("samples.txt")
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|
|
228 un = unique(test$Sample)
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|
|
229 un = paste(un, sep="\n")
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|
|
230 writeLines(un, sampleFile)
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|
|
231 close(sampleFile)
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|
47
|
232
|
|
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233
|
|
|
234 if("Replicate" %in% colnames(test))
|
|
|
235 {
|
|
|
236 clonalityFrame = PROD
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|
|
237 clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":"))
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|
|
238 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ]
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|
51
|
239 write.table(clonalityFrame, "clonalityComplete.tsv", sep="\t",quote=F,row.names=F,col.names=T)
|
|
49
|
240
|
|
|
241 ClonalitySampleReplicatePrint <- function(dat){
|
|
51
|
242 write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".tsv", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
|
|
49
|
243 }
|
|
|
244
|
|
|
245 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
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|
|
246 lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
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|
|
247
|
|
|
248 ClonalitySamplePrint <- function(dat){
|
|
51
|
249 write.table(dat, paste("clonality_", unique(dat$Sample) , ".tsv", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
|
|
49
|
250 }
|
|
|
251
|
|
|
252 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
|
|
|
253 lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
|
|
|
254
|
|
47
|
255 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "VDJCDR3")])
|
|
|
256 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
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257 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
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258 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
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259 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
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260
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261 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
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262 tcct = textConnection(ct)
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263 CT = read.table(tcct, sep="\t", header=TRUE)
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264 close(tcct)
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265 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
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266 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
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267
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268 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "VDJCDR3")])
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269 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
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270 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
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271
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272 ReplicatePrint <- function(dat){
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273 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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274 }
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275
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276 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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277 lapply(ReplicateSplit, FUN=ReplicatePrint)
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278
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279 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")])
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280 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
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281
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282
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283 ReplicateSumPrint <- function(dat){
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284 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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285 }
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286
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287 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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288 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
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289
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290 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
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291 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
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53
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292 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
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47
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293 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
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294 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
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295
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296 ClonalityScorePrint <- function(dat){
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297 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
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298 }
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299
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300 clonalityScore = clonalFreqCount[c("Sample", "Result")]
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301 clonalityScore = unique(clonalityScore)
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302
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303 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
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304 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
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305
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306 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
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307
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308
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309
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310 ClonalityOverviewPrint <- function(dat){
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311 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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312 }
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313
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314 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
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315 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
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316 }
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