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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 locus = args[6]
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11 selection = args[7]
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12
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13
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14
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15 if (!("gridExtra" %in% rownames(installed.packages()))) {
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16 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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17 }
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18 library(gridExtra)
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19 if (!("ggplot2" %in% rownames(installed.packages()))) {
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20 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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21 }
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22 require(ggplot2)
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23 if (!("plyr" %in% rownames(installed.packages()))) {
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24 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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25 }
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26 require(plyr)
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27
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28 if (!("data.table" %in% rownames(installed.packages()))) {
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29 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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30 }
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31 library(data.table)
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32
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33 if (!("reshape2" %in% rownames(installed.packages()))) {
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34 install.packages("reshape2", repos="http://cran.xl-mirror.nl/")
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35 }
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36 library(reshape2)
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37
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38
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39 test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="")
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40
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41 test = test[test$Sample != "",]
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42
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43 test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene)
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44 test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene)
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45 test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene)
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46
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47 #test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":"))
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48 test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":"))
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49
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50 PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ]
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51 if("Functionality" %in% colnames(test)) {
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52 PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ]
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53 }
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54
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55 NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ]
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56
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57 #PRODF = PROD[ -1]
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58
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59 PRODF = PROD
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60
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61 #PRODF = unique(PRODF)
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62
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63
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64
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65 if(selection == "unique"){
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66 PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ]
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67 }
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68
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69 PRODFV = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.V.Gene")])
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70 PRODFV$Length = as.numeric(PRODFV$Length)
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71 Total = 0
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72 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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73 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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74 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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75
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76 PRODFD = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.D.Gene")])
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77 PRODFD$Length = as.numeric(PRODFD$Length)
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78 Total = 0
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79 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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80 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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81 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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82
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83 PRODFJ = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.J.Gene")])
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84 PRODFJ$Length = as.numeric(PRODFJ$Length)
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85 Total = 0
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86 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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87 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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88 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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89
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90
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91 if(species == "human"){
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92 if(locus == "igh"){
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93 cat("human igh")
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94 } else if (locus == "igk"){
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95 cat("human igk")
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96 } else if (locus == "igl"){
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97 cat("human igl")
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98 }
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99 } else if (species == "mouse"){
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100 if(locus == "igh"){
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101 cat("mouse igh")
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102 } else if (locus == "igk"){
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103 cat("mouse igk")
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104 } else if (locus == "igl"){
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105 cat("mouse igl")
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106 }
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107 }
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108
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109 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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110 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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111 J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6")
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112
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113
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114
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115
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116 tcV = textConnection(V)
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117 Vchain = read.table(tcV, sep="\t", header=TRUE)
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118 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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119 close(tcV)
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120
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121
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122 tcD = textConnection(D)
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123 Dchain = read.table(tcD, sep="\t", header=TRUE)
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124 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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125 close(tcD)
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126
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127
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128
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129 tcJ = textConnection(J)
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130 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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131 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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132 close(tcJ)
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133
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134 setwd(outDir)
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135
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136 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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137
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138 pV = ggplot(PRODFV)
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139 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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140 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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141 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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142
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143 png("VPlot.png",width = 1280, height = 720)
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144 pV
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145 dev.off();
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146
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147 pD = ggplot(PRODFD)
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148 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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149 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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150 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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151
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152 png("DPlot.png",width = 800, height = 600)
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153 pD
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154 dev.off();
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155
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156 pJ = ggplot(PRODFJ)
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157 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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158 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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159 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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160
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161 png("JPlot.png",width = 800, height = 600)
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162 pJ
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163 dev.off();
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164
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165 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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166 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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167 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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168 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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169 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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170 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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171 VPlot = ggplot(VGenes)
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172 VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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173 ggtitle("Distribution of V gene families") +
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174 ylab("Percentage of sequences")
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175 png("VFPlot.png")
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176 VPlot
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177 dev.off();
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178 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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179
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180 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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181 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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182 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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183 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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184 DGenes = merge(DGenes, TotalPerSample, by="Sample")
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185 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
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186 DPlot = ggplot(DGenes)
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187 DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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188 ggtitle("Distribution of D gene families") +
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189 ylab("Percentage of sequences")
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190 png("DFPlot.png")
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191 DPlot
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192 dev.off();
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193 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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194
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195 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
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196 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
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197 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
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198 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
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199 JGenes = merge(JGenes, TotalPerSample, by="Sample")
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200 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
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201 JPlot = ggplot(JGenes)
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202 JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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203 ggtitle("Distribution of J gene families") +
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204 ylab("Percentage of sequences")
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205 png("JFPlot.png")
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206 JPlot
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207 dev.off();
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208 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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209
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210 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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211 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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212 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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213 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
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214 CDR3LengthPlot = ggplot(CDR3Length)
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215 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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216 ggtitle("Length distribution of CDR3") +
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217 xlab("CDR3 Length") +
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218 ylab("Percentage of sequences")
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219 png("CDR3LengthPlot.png",width = 1280, height = 720)
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220 CDR3LengthPlot
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221 dev.off()
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222 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
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223
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224 revVchain = Vchain
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225 revDchain = Dchain
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226 revVchain$chr.orderV = rev(revVchain$chr.orderV)
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227 revDchain$chr.orderD = rev(revDchain$chr.orderD)
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228
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229 plotVD <- function(dat){
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230 if(length(dat[,1]) == 0){
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231 return()
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232 }
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233 img = ggplot() +
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234 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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235 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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236 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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237 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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238 xlab("D genes") +
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239 ylab("V Genes")
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240
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241 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
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242 print(img)
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243
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244 dev.off()
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245 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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246 }
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247
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248 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
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249
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250 VandDCount$l = log(VandDCount$Length)
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251 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
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252 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
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253 VandDCount$relLength = VandDCount$l / VandDCount$max
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254
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255 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(test$Sample))
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256
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257 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
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258 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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259 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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260 VDList = split(completeVD, f=completeVD[,"Sample"])
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261
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262 lapply(VDList, FUN=plotVD)
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263
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264
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265
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266 plotVJ <- function(dat){
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267 if(length(dat[,1]) == 0){
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268 return()
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269 }
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270 img = ggplot() +
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271 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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272 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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273 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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274 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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275 xlab("J genes") +
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276 ylab("V Genes")
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277
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278 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
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279 print(img)
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280 dev.off()
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281 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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282 }
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283
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284 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
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285
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286 VandJCount$l = log(VandJCount$Length)
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287 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
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288 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
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289 VandJCount$relLength = VandJCount$l / VandJCount$max
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290
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291 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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292
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293 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
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294 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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295 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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296 VJList = split(completeVJ, f=completeVJ[,"Sample"])
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297 lapply(VJList, FUN=plotVJ)
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298
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299 plotDJ <- function(dat){
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300 if(length(dat[,1]) == 0){
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301 return()
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302 }
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303 img = ggplot() +
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304 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
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305 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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306 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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307 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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308 xlab("J genes") +
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309 ylab("D Genes")
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310
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311 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
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312 print(img)
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313 dev.off()
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314 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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315 }
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316
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317 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
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318
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319 DandJCount$l = log(DandJCount$Length)
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320 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
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321 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
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322 DandJCount$relLength = DandJCount$l / DandJCount$max
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323
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324 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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325
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326 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
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327 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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328 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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329 DJList = split(completeDJ, f=completeDJ[,"Sample"])
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330 lapply(DJList, FUN=plotDJ)
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331
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332
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333 sampleFile <- file("samples.txt")
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334 un = unique(test$Sample)
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335 un = paste(un, sep="\n")
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336 writeLines(un, sampleFile)
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337 close(sampleFile)
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338
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339
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340 if("Replicate" %in% colnames(test))
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341 {
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342 clonalityFrame = PROD
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343 clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":"))
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344 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ]
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345 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
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346
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347 ClonalitySampleReplicatePrint <- function(dat){
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348 write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
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349 }
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350
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351 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
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352 #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
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353
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354 ClonalitySamplePrint <- function(dat){
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355 write.table(dat, paste("clonality_", unique(dat$Sample) , ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
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356 }
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357
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358 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
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359 #lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
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360
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361 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "VDJCDR3")])
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362 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
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363 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
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364 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
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365 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
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366
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367 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
368 tcct = textConnection(ct)
|
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|
369 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
370 close(tcct)
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|
|
371 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
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|
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372 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
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|
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373
|
|
|
374 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "VDJCDR3")])
|
|
|
375 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
376 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
377 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
378
|
|
|
379 ReplicatePrint <- function(dat){
|
|
|
380 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
381 }
|
|
|
382
|
|
|
383 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
384 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
385
|
|
|
386 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")])
|
|
|
387 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
388
|
|
|
389
|
|
|
390 ReplicateSumPrint <- function(dat){
|
|
|
391 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
392 }
|
|
|
393
|
|
|
394 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
395 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
396
|
|
|
397 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
398 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
399 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
400 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
401 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
402
|
|
|
403 ClonalityScorePrint <- function(dat){
|
|
|
404 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
405 }
|
|
|
406
|
|
|
407 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
408 clonalityScore = unique(clonalityScore)
|
|
|
409
|
|
|
410 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
411 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
412
|
|
|
413 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
414
|
|
|
415
|
|
|
416
|
|
|
417 ClonalityOverviewPrint <- function(dat){
|
|
|
418 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
419 }
|
|
|
420
|
|
|
421 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
422 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
423 }
|
|
|
424
|
|
|
425 if("Functionality" %in% colnames(test))
|
|
|
426 {
|
|
|
427 newData = data.frame(data.table(PROD)[,list(unique=.N,
|
|
|
428 VH.DEL=mean(X3V.REGION.trimmed.nt.nb),
|
|
|
429 P1=mean(P3V.nt.nb),
|
|
|
430 N1=mean(N1.REGION.nt.nb),
|
|
|
431 P2=mean(P5D.nt.nb),
|
|
|
432 DEL.DH=mean(X5D.REGION.trimmed.nt.nb),
|
|
|
433 DH.DEL=mean(X3D.REGION.trimmed.nt.nb),
|
|
|
434 P3=mean(P3D.nt.nb),
|
|
|
435 N2=mean(N2.REGION.nt.nb),
|
|
|
436 P4=mean(P5J.nt.nb),
|
|
|
437 DEL.JH=mean(X5J.REGION.trimmed.nt.nb),
|
|
|
438 Total.Del=( mean(X3V.REGION.trimmed.nt.nb) +
|
|
|
439 mean(X5D.REGION.trimmed.nt.nb) +
|
|
|
440 mean(X3D.REGION.trimmed.nt.nb) +
|
|
|
441 mean(X5J.REGION.trimmed.nt.nb)),
|
|
|
442
|
|
|
443 Total.N=( mean(N1.REGION.nt.nb) +
|
|
|
444 mean(N2.REGION.nt.nb)),
|
|
|
445
|
|
|
446 Total.P=( mean(P3V.nt.nb) +
|
|
|
447 mean(P5D.nt.nb) +
|
|
|
448 mean(P3D.nt.nb) +
|
|
|
449 mean(P5J.nt.nb))),
|
|
|
450 by=c("Sample")])
|
|
|
451 write.table(newData, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
452 }
|
