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7
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1 # ---------------------- load/install packages ----------------------
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2
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3 if (!("gridExtra" %in% rownames(installed.packages()))) {
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4 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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5 }
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6 library(gridExtra)
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7 if (!("ggplot2" %in% rownames(installed.packages()))) {
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8 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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9 }
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10 library(ggplot2)
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11 if (!("plyr" %in% rownames(installed.packages()))) {
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12 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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13 }
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14 library(plyr)
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15
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16 if (!("data.table" %in% rownames(installed.packages()))) {
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17 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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18 }
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19 library(data.table)
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20
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21 if (!("reshape2" %in% rownames(installed.packages()))) {
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22 install.packages("reshape2", repos="http://cran.xl-mirror.nl/")
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23 }
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24 library(reshape2)
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25
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26 # ---------------------- parameters ----------------------
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27
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28 args <- commandArgs(trailingOnly = TRUE)
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29
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30 infile = args[1] #path to input file
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31 outfile = args[2] #path to output file
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32 outdir = args[3] #path to output folder (html/images/data)
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33 clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering
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34 species = args[5] #human or mouse
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35 locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD
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36 filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no)
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37
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38 # ---------------------- Data preperation ----------------------
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39
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40 inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="")
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41
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42 setwd(outdir)
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43
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44 # remove weird rows
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45 inputdata = inputdata[inputdata$Sample != "",]
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46
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47 #remove the allele from the V,D and J genes
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48 inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
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49 inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
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50 inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
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51 inputdata$clonaltype = 1:nrow(inputdata)
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52 PRODF = inputdata
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9
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53 UNPROD = inputdata
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7
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54 if(filterproductive){
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55 if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
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56 PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
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9
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57 UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ]
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7
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58 } else {
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59 PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
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9
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60 UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
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7
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61 }
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62 }
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63
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64 #remove duplicates based on the clonaltype
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65 if(clonaltype != "none"){
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66 PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
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67 PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
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9
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68 UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":"))
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69 UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ]
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7
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70 }
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71
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72 PRODF$freq = 1
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73
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74 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
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75 PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
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76 PRODF$freq = gsub("_.*", "", PRODF$freq)
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77 PRODF$freq = as.numeric(PRODF$freq)
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78 if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence
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79 PRODF$freq = 1
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80 }
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81 }
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82
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83
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84
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85 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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86 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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87
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88 #write the samples to a file
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89 sampleFile <- file("samples.txt")
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90 un = unique(inputdata$Sample)
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91 un = paste(un, sep="\n")
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92 writeLines(un, sampleFile)
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93 close(sampleFile)
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94
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95 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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96
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97 inputdata.dt = data.table(inputdata) #for speed
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98
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99 ct = unlist(strsplit(clonaltype, ","))
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100 if(clonaltype == "none"){
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101 ct = c("ID")
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102 }
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103
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104 inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
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105 samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
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106 frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
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107
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108
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109 sample_productive_count = inputdata.dt[, list(All=.N,
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110 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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111 perc_prod = 1,
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112 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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113 perc_prod_un = 1,
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114 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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115 perc_unprod = 1,
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116 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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117 perc_unprod_un = 1),
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118 by=c("Sample")]
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119
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120 sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
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121 sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
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122
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123 sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
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124 sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
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125
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126
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127 sample_replicate_productive_count = inputdata.dt[, list(All=.N,
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128 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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129 perc_prod = 1,
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130 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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131 perc_prod_un = 1,
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132 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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133 perc_unprod = 1,
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134 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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135 perc_unprod_un = 1),
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136 by=c("samples_replicates")]
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137
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138 sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
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139 sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
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140
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141 sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
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142 sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
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143
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144 setnames(sample_replicate_productive_count, colnames(sample_productive_count))
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145
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146 counts = rbind(sample_replicate_productive_count, sample_productive_count)
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147 counts = counts[order(counts$Sample),]
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148
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149 write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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150
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151 # ---------------------- Frequency calculation for V, D and J ----------------------
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152
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153 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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154 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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155 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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156 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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157
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158 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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159 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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160 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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161 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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162
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163 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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164 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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165 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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166 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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167
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168 # ---------------------- Setting up the gene names for the different T/B, human/mouse and locus ----------------------
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169
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170 V = c("v.name\tchr.orderV\n")
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171 D = c("v.name\tchr.orderD\n")
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172 J = c("v.name\tchr.orderJ\n")
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173
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174 if(species == "human"){
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175 if(locus == "trb"){
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176 V = c("v.name\tchr.orderV\nTRBV2\t1\nTRBV3-1\t2\nTRBV4-1\t3\nTRBV5-1\t4\nTRBV6-1\t5\nTRBV4-2\t6\nTRBV6-2\t7\nTRBV4-3\t8\nTRBV6-3\t9\nTRBV7-2\t10\nTRBV6-4\t11\nTRBV7-3\t12\nTRBV9\t13\nTRBV10-1\t14\nTRBV11-1\t15\nTRBV10-2\t16\nTRBV11-2\t17\nTRBV6-5\t18\nTRBV7-4\t19\nTRBV5-4\t20\nTRBV6-6\t21\nTRBV5-5\t22\nTRBV7-6\t23\nTRBV5-6\t24\nTRBV6-8\t25\nTRBV7-7\t26\nTRBV6-9\t27\nTRBV7-8\t28\nTRBV5-8\t29\nTRBV7-9\t30\nTRBV13\t31\nTRBV10-3\t32\nTRBV11-3\t33\nTRBV12-3\t34\nTRBV12-4\t35\nTRBV12-5\t36\nTRBV14\t37\nTRBV15\t38\nTRBV16\t39\nTRBV18\t40\nTRBV19\t41\nTRBV20-1\t42\nTRBV24-1\t43\nTRBV25-1\t44\nTRBV27\t45\nTRBV28\t46\nTRBV29-1\t47\nTRBV30\t48")
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177 D = c("v.name\tchr.orderD\nTRBD1\t1\nTRBD2\t2\n")
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178 J = c("v.name\tchr.orderJ\nTRBJ1-1\t1\nTRBJ1-2\t2\nTRBJ1-3\t3\nTRBJ1-4\t4\nTRBJ1-5\t5\nTRBJ1-6\t6\nTRBJ2-1\t7\nTRBJ2-2\t8\nTRBJ2-3\t9\nTRBJ2-4\t10\nTRBJ2-5\t11\nTRBJ2-6\t12\nTRBJ2-7\t13")
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179 } else if (locus == "tra"){
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180 V = c("v.name\tchr.orderVTRAV1-1\t1\nTRAV1-2\t2\nTRAV2\t3\nTRAV3\t4\nTRAV4\t5\nTRAV5\t6\nTRAV6\t7\nTRAV7\t8\nTRAV8-1\t9\nTRAV9-1\t10\nTRAV10\t11\nTRAV12-1\t12\nTRAV8-2\t13\nTRAV8-3\t14\nTRAV13-1\t15\nTRAV12-2\t16\nTRAV8-4\t17\nTRAV13-2\t18\nTRAV14/DV4\t19\nTRAV9-2\t20\nTRAV12-3\t21\nTRAV8-6\t22\nTRAV16\t23\nTRAV17\t24\nTRAV18\t25\nTRAV19\t26\nTRAV20\t27\nTRAV21\t28\nTRAV22\t29\nTRAV23/DV6\t30\nTRAV24\t31\nTRAV25\t32\nTRAV26-1\t33\nTRAV27\t34\nTRAV29/DV5\t35\nTRAV30\t36\nTRAV26-2\t37\nTRAV34\t38\nTRAV35\t39\nTRAV36/DV7\t40\nTRAV38-1\t41\nTRAV38-2/DV8\t42\nTRAV39\t43\nTRAV40\t44\nTRAV41\t45\n")
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181 D = c("v.name\tchr.orderD\n")
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182 J = c("v.name\tchr.orderJ\nTRAJ57\t1\nTRAJ56\t2\nTRAJ54\t3\nTRAJ53\t4\nTRAJ52\t5\nTRAJ50\t6\nTRAJ49\t7\nTRAJ48\t8\nTRAJ47\t9\nTRAJ46\t10\nTRAJ45\t11\nTRAJ44\t12\nTRAJ43\t13\nTRAJ42\t14\nTRAJ41\t15\nTRAJ40\t16\nTRAJ39\t17\nTRAJ38\t18\nTRAJ37\t19\nTRAJ36\t20\nTRAJ34\t21\nTRAJ33\t22\nTRAJ32\t23\nTRAJ31\t24\nTRAJ30\t25\nTRAJ29\t26\nTRAJ28\t27\nTRAJ27\t28\nTRAJ26\t29\nTRAJ24\t30\nTRAJ23\t31\nTRAJ22\t32\nTRAJ21\t33\nTRAJ20\t34\nTRAJ18\t35\nTRAJ17\t36\nTRAJ16\t37\nTRAJ15\t38\nTRAJ14\t39\nTRAJ13\t40\nTRAJ12\t41\nTRAJ11\t42\nTRAJ10\t43\nTRAJ9\t44\nTRAJ8\t45\nTRAJ7\t46\nTRAJ6\t47\nTRAJ5\t48\nTRAJ4\t49\nTRAJ3\t50")
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183 } else if (locus == "trg"){
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184 V = c("v.name\tchr.orderV\nTRGV9\t1\nTRGV8\t2\nTRGV5\t3\nTRGV4\t4\nTRGV3\t5\nTRGV2\t6")
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185 D = c("v.name\tchr.orderD\n")
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186 J = c("v.name\tchr.orderJ\nTRGJ2\t1\nTRGJP2\t2\nTRGJ1\t3\nTRGJP1\t4")
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187 } else if (locus == "trd"){
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188 V = c("v.name\tchr.orderV\nTRDV1\t1\nTRDV2\t2\nTRDV3\t3")
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189 D = c("v.name\tchr.orderD\nTRDD1\t1\nTRDD2\t2\nTRDD3\t3")
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190 J = c("v.name\tchr.orderJ\nTRDJ1\t1\nTRDJ4\t2\nTRDJ2\t3\nTRDJ3\t4")
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191 } else if(locus == "igh"){
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192 V = c("v.name\tchr.orderV\nIGHV3-74\t1\nIGHV3-73\t2\nIGHV3-72\t3\nIGHV2-70\t4\nIGHV1-69D\t5\nIGHV1-69-2\t6\nIGHV2-70D\t7\nIGHV1-69\t8\nIGHV3-66\t9\nIGHV3-64\t10\nIGHV4-61\t11\nIGHV4-59\t12\nIGHV1-58\t13\nIGHV3-53\t14\nIGHV5-51\t15\nIGHV3-49\t16\nIGHV3-48\t17\nIGHV1-46\t18\nIGHV1-45\t19\nIGHV3-43\t20\nIGHV4-39\t21\nIGHV3-43D\t22\nIGHV4-38-2\t23\nIGHV4-34\t24\nIGHV3-33\t25\nIGHV4-31\t26\nIGHV3-30-5\t27\nIGHV4-30-4\t28\nIGHV3-30-3\t29\nIGHV4-30-2\t30\nIGHV4-30-1\t31\nIGHV3-30\t32\nIGHV4-28\t33\nIGHV2-26\t34\nIGHV1-24\t35\nIGHV3-23D\t36\nIGHV3-23\t37\nIGHV3-21\t38\nIGHV3-20\t39\nIGHV1-18\t40\nIGHV3-15\t41\nIGHV3-13\t42\nIGHV3-11\t43\nIGHV5-10-1\t44\nIGHV3-9\t45\nIGHV1-8\t46\nIGHV3-64D\t47\nIGHV3-7\t48\nIGHV2-5\t49\nIGHV7-4-1\t50\nIGHV4-4\t51\nIGHV1-3\t52\nIGHV1-2\t53\nIGHV6-1\t54")
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193 D = c("v.name\tchr.orderD\nIGHD1-7\t1\nIGHD2-8\t2\nIGHD3-9\t3\nIGHD3-10\t4\nIGHD5-12\t5\nIGHD6-13\t6\nIGHD2-15\t7\nIGHD3-16\t8\nIGHD4-17\t9\nIGHD5-18\t10\nIGHD6-19\t11\nIGHD1-20\t12\nIGHD2-21\t13\nIGHD3-22\t14\nIGHD5-24\t15\nIGHD6-25\t16\nIGHD1-26\t17\nIGHD7-27\t18")
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194 J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6")
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195 } else if (locus == "igk"){
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196 V = c("v.name\tchr.orderV\nIGKV3D-7\t1\nIGKV1D-8\t2\nIGKV1D-43\t3\nIGKV3D-11\t4\nIGKV1D-12\t5\nIGKV1D-13\t6\nIGKV3D-15\t7\nIGKV1D-16\t8\nIGKV1D-17\t9\nIGKV3D-20\t10\nIGKV2D-26\t11\nIGKV2D-28\t12\nIGKV2D-29\t13\nIGKV2D-30\t14\nIGKV1D-33\t15\nIGKV1D-39\t16\nIGKV2D-40\t17\nIGKV2-40\t18\nIGKV1-39\t19\nIGKV1-33\t20\nIGKV2-30\t21\nIGKV2-29\t22\nIGKV2-28\t23\nIGKV1-27\t24\nIGKV2-24\t25\nIGKV3-20\t26\nIGKV1-17\t27\nIGKV1-16\t28\nIGKV3-15\t29\nIGKV1-13\t30\nIGKV1-12\t31\nIGKV3-11\t32\nIGKV1-9\t33\nIGKV1-8\t34\nIGKV1-6\t35\nIGKV1-5\t36\nIGKV5-2\t37\nIGKV4-1\t38")
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197 D = c("v.name\tchr.orderD\n")
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198 J = c("v.name\tchr.orderJ\nIGKJ1\t1\nIGKJ2\t2\nIGKJ3\t3\nIGKJ4\t4\nIGKJ5\t5")
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199 } else if (locus == "igl"){
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200 V = c("v.name\tchr.orderV\nIGLV4-69\t1\nIGLV8-61\t2\nIGLV4-60\t3\nIGLV6-57\t4\nIGLV5-52\t5\nIGLV1-51\t6\nIGLV9-49\t7\nIGLV1-47\t8\nIGLV7-46\t9\nIGLV5-45\t10\nIGLV1-44\t11\nIGLV7-43\t12\nIGLV1-41\t13\nIGLV1-40\t14\nIGLV5-39\t15\nIGLV5-37\t16\nIGLV1-36\t17\nIGLV3-27\t18\nIGLV3-25\t19\nIGLV2-23\t20\nIGLV3-22\t21\nIGLV3-21\t22\nIGLV3-19\t23\nIGLV2-18\t24\nIGLV3-16\t25\nIGLV2-14\t26\nIGLV3-12\t27\nIGLV2-11\t28\nIGLV3-10\t29\nIGLV3-9\t30\nIGLV2-8\t31\nIGLV4-3\t32\nIGLV3-1\t33")
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201 D = c("v.name\tchr.orderD\n")
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202 J = c("v.name\tchr.orderJ\nIGLJ1\t1\nIGLJ2\t2\nIGLJ3\t3\nIGLJ6\t4\nIGLJ7\t5")
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203 }
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|
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204 } else if (species == "mouse"){
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205 if(locus == "trb"){
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206 V = c("v.name\tchr.orderV\nTRBV1\t1\nTRBV2\t2\nTRBV3\t3\nTRBV4\t4\nTRBV5\t5\nTRBV12-1\t6\nTRBV13-1\t7\nTRBV12-2\t8\nTRBV13-2\t9\nTRBV13-3\t10\nTRBV14\t11\nTRBV15\t12\nTRBV16\t13\nTRBV17\t14\nTRBV19\t15\nTRBV20\t16\nTRBV23\t17\nTRBV24\t18\nTRBV26\t19\nTRBV29\t20\nTRBV30\t21\nTRBV31\t22")
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207 D = c("v.name\tchr.orderD\nTRBD1\t1\nTRBD2\t2")
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208 J = c("v.name\tchr.orderJ\nTRBJ1-1\t1\nTRBJ1-2\t2\nTRBJ1-3\t3\nTRBJ1-4\t4\nTRBJ1-5\t5\nTRBJ2-1\t6\nTRBJ2-2\t7\nTRBJ2-3\t8\nTRBJ2-4\t9\nTRBJ2-5\t10\nTRBJ2-6\t11\nTRBJ2-7\t12")
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209 } else if (locus == "tra"){
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210 cat("mouse tra not yet implemented")
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211 } else if (locus == "trg"){
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212 cat("mouse trg not yet implemented")
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213 } else if (locus == "trd"){
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214 cat("mouse trd not yet implemented")
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215 } else if(locus == "igh"){
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216 cat("mouse igh not yet implemented")
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217 } else if (locus == "igk"){
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218 cat("mouse igk not yet implemented")
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219 } else if (locus == "igl"){
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220 cat("mouse igl not yet implemented")
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221 }
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222 }
|
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223
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224 useD = TRUE
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225 if(species == "human" && locus == "tra"){
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226 useD = FALSE
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227 cat("No D Genes in this species/locus")
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228 }
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229
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230 # ---------------------- load the gene names into a data.frame and merge with the frequency count ----------------------
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231
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232 tcV = textConnection(V)
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233 Vchain = read.table(tcV, sep="\t", header=TRUE)
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234 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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235 close(tcV)
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236
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237 tcD = textConnection(D)
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238 Dchain = read.table(tcD, sep="\t", header=TRUE)
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239 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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240 close(tcD)
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241
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242 tcJ = textConnection(J)
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243 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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244 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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245 close(tcJ)
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246
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247 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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248
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249 pV = ggplot(PRODFV)
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250 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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251 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
|
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252 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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253
|
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254 png("VPlot.png",width = 1280, height = 720)
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255 pV
|
|
|
256 dev.off();
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257
|
|
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258 if(useD){
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259 pD = ggplot(PRODFD)
|
|
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260 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))
|
|
|
261 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
|
|
|
262 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
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263
|
|
|
264 png("DPlot.png",width = 800, height = 600)
|
|
|
265 print(pD)
|
|
|
266 dev.off();
|
|
|
267 }
|
|
|
268
|
|
|
269 pJ = ggplot(PRODFJ)
|
|
|
270 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))
|
|
|
271 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
|
|
|
272 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
273
|
|
|
274 png("JPlot.png",width = 800, height = 600)
|
|
|
275 pJ
|
|
|
276 dev.off();
|
|
|
277
|
|
|
278 pJ = ggplot(PRODFJ)
|
|
|
279 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))
|
|
|
280 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
|
|
|
281 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
282
|
|
|
283 png("JPlot.png",width = 800, height = 600)
|
|
|
284 pJ
|
|
|
285 dev.off();
|
|
|
286
|
|
|
287 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
|
|
|
288
|
|
|
289 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
|
|
|
290 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
|
|
|
291 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
|
|
|
292 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
293 VGenes = merge(VGenes, TotalPerSample, by="Sample")
|
|
|
294 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
|
|
|
295 VPlot = ggplot(VGenes)
|
|
|
296 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)) +
|
|
|
297 ggtitle("Distribution of V gene families") +
|
|
|
298 ylab("Percentage of sequences")
|
|
|
299 png("VFPlot.png")
|
|
|
300 VPlot
|
|
|
301 dev.off();
|
|
|
302 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
303
|
|
|
304 if(useD){
|
|
|
305 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
|
|
|
306 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
|
|
|
307 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
|
|
|
308 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
309 DGenes = merge(DGenes, TotalPerSample, by="Sample")
|
|
|
310 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
|
|
|
311 DPlot = ggplot(DGenes)
|
|
|
312 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)) +
|
|
|
313 ggtitle("Distribution of D gene families") +
|
|
|
314 ylab("Percentage of sequences")
|
|
|
315 png("DFPlot.png")
|
|
|
316 print(DPlot)
|
|
|
317 dev.off();
|
|
|
318 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
319 }
|
|
|
320
|
|
|
321 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
|
|
|
322 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
|
|
|
323 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
|
|
|
324 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
325 JGenes = merge(JGenes, TotalPerSample, by="Sample")
|
|
|
326 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
|
|
|
327 JPlot = ggplot(JGenes)
|
|
|
328 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)) +
|
|
|
329 ggtitle("Distribution of J gene families") +
|
|
|
330 ylab("Percentage of sequences")
|
|
|
331 png("JFPlot.png")
|
|
|
332 JPlot
|
|
|
333 dev.off();
|
|
|
334 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
335
|
|
|
336 # ---------------------- Plotting the cdr3 length ----------------------
|
|
|
337
|
|
|
338 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
|
|
|
339 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
340 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
|
|
|
341 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
|
|
|
342 CDR3LengthPlot = ggplot(CDR3Length)
|
|
|
343 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)) +
|
|
|
344 ggtitle("Length distribution of CDR3") +
|
|
|
345 xlab("CDR3 Length") +
|
|
|
346 ylab("Percentage of sequences")
|
|
|
347 png("CDR3LengthPlot.png",width = 1280, height = 720)
|
|
|
348 CDR3LengthPlot
|
|
|
349 dev.off()
|
|
|
350 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
351
|
|
|
352 # ---------------------- Plot the heatmaps ----------------------
|
|
|
353
|
|
|
354
|
|
|
355 #get the reverse order for the V and D genes
|
|
|
356 revVchain = Vchain
|
|
|
357 revDchain = Dchain
|
|
|
358 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
359 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
|
|
360
|
|
|
361 if(useD){
|
|
|
362 plotVD <- function(dat){
|
|
|
363 if(length(dat[,1]) == 0){
|
|
|
364 return()
|
|
|
365 }
|
|
|
366 img = ggplot() +
|
|
|
367 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
368 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
369 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
370 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
371 xlab("D genes") +
|
|
|
372 ylab("V Genes")
|
|
|
373
|
|
|
374 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
375 print(img)
|
|
|
376 dev.off()
|
|
|
377 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)
|
|
|
378 }
|
|
|
379
|
|
|
380 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
|
381
|
|
|
382 VandDCount$l = log(VandDCount$Length)
|
|
|
383 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
384 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
385 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
|
386
|
|
|
387 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
388
|
|
|
389 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
|
|
|
390 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
391 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
392 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
393
|
|
|
394 lapply(VDList, FUN=plotVD)
|
|
|
395 }
|
|
|
396
|
|
|
397 plotVJ <- function(dat){
|
|
|
398 if(length(dat[,1]) == 0){
|
|
|
399 return()
|
|
|
400 }
|
|
|
401 cat(paste(unique(dat[3])[1,1]))
|
|
|
402 img = ggplot() +
|
|
|
403 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
404 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
405 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
406 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
407 xlab("J genes") +
|
|
|
408 ylab("V Genes")
|
|
|
409
|
|
|
410 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
411 print(img)
|
|
|
412 dev.off()
|
|
|
413 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)
|
|
|
414 }
|
|
|
415
|
|
|
416 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
417
|
|
|
418 VandJCount$l = log(VandJCount$Length)
|
|
|
419 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
420 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
421 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
422
|
|
|
423 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
424
|
|
|
425 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
426 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
427 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
428 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
429 lapply(VJList, FUN=plotVJ)
|
|
|
430
|
|
|
431 if(useD){
|
|
|
432 plotDJ <- function(dat){
|
|
|
433 if(length(dat[,1]) == 0){
|
|
|
434 return()
|
|
|
435 }
|
|
|
436 img = ggplot() +
|
|
|
437 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
438 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
439 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
440 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
441 xlab("J genes") +
|
|
|
442 ylab("D Genes")
|
|
|
443
|
|
|
444 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
445 print(img)
|
|
|
446 dev.off()
|
|
|
447 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)
|
|
|
448 }
|
|
|
449
|
|
|
450
|
|
|
451 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
452
|
|
|
453 DandJCount$l = log(DandJCount$Length)
|
|
|
454 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
455 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
456 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
457
|
|
|
458 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
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459
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460 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
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461 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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462 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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463 DJList = split(completeDJ, f=completeDJ[,"Sample"])
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464 lapply(DJList, FUN=plotDJ)
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465 }
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466
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467
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468 # ---------------------- calculating the clonality score ----------------------
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469
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470 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
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471 {
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472 clonalityFrame = inputdata
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473 if(clonaltype != "none"){
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474 clonalityFrame$ReplicateConcat = paste(clonalityFrame$clonaltype, clonalityFrame$Sample, clonalityFrame$Replicate, sep = ":")
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475 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ]
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476 }
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477 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
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478
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479 ClonalitySampleReplicatePrint <- function(dat){
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480 write.table(dat, paste("clonality_", unique(inputdata$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
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481 }
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482
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483 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
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484 #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
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485
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486 ClonalitySamplePrint <- function(dat){
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487 write.table(dat, paste("clonality_", unique(inputdata$Sample) , ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
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488 }
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489
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490 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
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491 #lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
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492
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493 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
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494 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
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495 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
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496 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
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497 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
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498
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499 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
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500 tcct = textConnection(ct)
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501 CT = read.table(tcct, sep="\t", header=TRUE)
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502 close(tcct)
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503 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
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504 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
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505
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506 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
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507 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
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508 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
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509 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
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510
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511 ReplicatePrint <- function(dat){
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512 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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513 }
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514
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515 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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516 lapply(ReplicateSplit, FUN=ReplicatePrint)
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517
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518 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")])
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519 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
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520
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521
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522 ReplicateSumPrint <- function(dat){
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523 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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524 }
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525
|
|
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526 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
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527 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
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528
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529 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
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530 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
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531 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
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|
|
532 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
533 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
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534
|
|
|
535 ClonalityScorePrint <- function(dat){
|
|
|
536 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
537 }
|
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538
|
|
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539 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
540 clonalityScore = unique(clonalityScore)
|
|
|
541
|
|
|
542 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
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543 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
544
|
|
|
545 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
546
|
|
|
547
|
|
|
548
|
|
|
549 ClonalityOverviewPrint <- function(dat){
|
|
|
550 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
551 }
|
|
|
552
|
|
|
553 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
554 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
555 }
|
|
|
556
|
|
|
557 imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
|
|
|
558 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
559 {
|
|
8
|
560 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
7
|
561 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
562 P1=mean(P3V.nt.nb, na.rm=T),
|
|
|
563 N1=mean(N1.REGION.nt.nb, na.rm=T),
|
|
|
564 P2=mean(P5D.nt.nb, na.rm=T),
|
|
|
565 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
566 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
567 P3=mean(P3D.nt.nb, na.rm=T),
|
|
|
568 N2=mean(N2.REGION.nt.nb, na.rm=T),
|
|
|
569 P4=mean(P5J.nt.nb, na.rm=T),
|
|
|
570 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
571 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
572 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
573 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
574 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
|
|
|
575
|
|
|
576 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
|
|
|
577 mean(N2.REGION.nt.nb, na.rm=T)),
|
|
|
578
|
|
|
579 Total.P=( mean(P3V.nt.nb, na.rm=T) +
|
|
|
580 mean(P5D.nt.nb, na.rm=T) +
|
|
|
581 mean(P3D.nt.nb, na.rm=T) +
|
|
|
582 mean(P5J.nt.nb, na.rm=T))),
|
|
|
583 by=c("Sample")])
|
|
9
|
584 write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
585
|
|
|
586 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
|
|
|
587 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
588 P1=mean(P3V.nt.nb, na.rm=T),
|
|
|
589 N1=mean(N1.REGION.nt.nb, na.rm=T),
|
|
|
590 P2=mean(P5D.nt.nb, na.rm=T),
|
|
|
591 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
592 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
593 P3=mean(P3D.nt.nb, na.rm=T),
|
|
|
594 N2=mean(N2.REGION.nt.nb, na.rm=T),
|
|
|
595 P4=mean(P5J.nt.nb, na.rm=T),
|
|
|
596 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
597 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
598 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
599 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
600 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
|
|
|
601
|
|
|
602 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
|
|
|
603 mean(N2.REGION.nt.nb, na.rm=T)),
|
|
|
604
|
|
|
605 Total.P=( mean(P3V.nt.nb, na.rm=T) +
|
|
|
606 mean(P5D.nt.nb, na.rm=T) +
|
|
|
607 mean(P3D.nt.nb, na.rm=T) +
|
|
|
608 mean(P5J.nt.nb, na.rm=T))),
|
|
|
609 by=c("Sample")])
|
|
|
610 write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
7
|
611 }
|
