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5
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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 if (!("lymphclon" %in% rownames(installed.packages()))) {
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27 install.packages("lymphclon", repos="http://cran.xl-mirror.nl/")
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28 }
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29 library(lymphclon)
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30
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31 # ---------------------- parameters ----------------------
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32
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33 args <- commandArgs(trailingOnly = TRUE)
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34
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35 infile = args[1] #path to input file
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36 outfile = args[2] #path to output file
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37 outdir = args[3] #path to output folder (html/images/data)
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38 clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering
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39 ct = unlist(strsplit(clonaltype, ","))
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40 species = args[5] #human or mouse
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41 locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD
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42 filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no)
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43 clonality_method = args[8]
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44
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45
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46 # ---------------------- Data preperation ----------------------
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47
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48 print("Report Clonality - Data preperation")
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49
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13
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50 inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="", stringsAsFactors=F)
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5
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51
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24
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52
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5
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53 print(paste("nrows: ", nrow(inputdata)))
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54
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55 setwd(outdir)
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56
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57 # remove weird rows
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58 inputdata = inputdata[inputdata$Sample != "",]
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59
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60 print(paste("nrows: ", nrow(inputdata)))
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61
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62 #remove the allele from the V,D and J genes
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63 inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
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64 inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
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65 inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
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66
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67 print(paste("nrows: ", nrow(inputdata)))
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68
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69 #filter uniques
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70 inputdata.removed = inputdata[NULL,]
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71
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72 print(paste("nrows: ", nrow(inputdata)))
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73
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74 inputdata$clonaltype = 1:nrow(inputdata)
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75
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76 #keep track of the count of sequences in samples or samples/replicates for the front page overview
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77 input.sample.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample")])
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78 input.rep.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample", "Replicate")])
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79
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80 PRODF = inputdata
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81 UNPROD = inputdata
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82 if(filterproductive){
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83 if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
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84 #PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
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85 PRODF = inputdata[inputdata$Functionality %in% c("productive (see comment)","productive"),]
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86
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87 PRODF.count = data.frame(data.table(PRODF)[, list(count=.N), by=c("Sample")])
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88
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89 UNPROD = inputdata[inputdata$Functionality %in% c("unproductive (see comment)","unproductive"), ]
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90 } else {
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91 PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
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92 UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
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93 }
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94 }
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95
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13
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96 for(i in 1:nrow(UNPROD)){
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97 if(!is.numeric(UNPROD[i,"CDR3.Length"])){
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98 UNPROD[i,"CDR3.Length"] = 0
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99 }
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100 }
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101
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5
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102 prod.sample.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample")])
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103 prod.rep.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample", "Replicate")])
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104
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105 unprod.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample")])
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106 unprod.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample", "Replicate")])
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107
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108 clonalityFrame = PRODF
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109
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110 #remove duplicates based on the clonaltype
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111 if(clonaltype != "none"){
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112 clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples
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113 PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
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114 PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
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115
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116 UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":"))
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117 UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ]
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118
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119 #again for clonalityFrame but with sample+replicate
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120 clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":"))
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121 clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":"))
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122 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ]
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123 }
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124
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125 prod.unique.sample.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample")])
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126 prod.unique.rep.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample", "Replicate")])
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127
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128 unprod.unique.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample")])
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129 unprod.unique.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample", "Replicate")])
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130
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131 PRODF$freq = 1
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132
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133 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
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134 PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
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135 PRODF$freq = gsub("_.*", "", PRODF$freq)
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136 PRODF$freq = as.numeric(PRODF$freq)
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137 if(any(is.na(PRODF$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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138 PRODF$freq = 1
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139 }
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140 }
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141
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8
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142 #make a names list with sample -> color
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143 naive.colors = c('blue4', 'darkred', 'olivedrab3', 'red', 'gray74', 'darkviolet', 'lightblue1', 'gold', 'chartreuse2', 'pink', 'Paleturquoise3', 'Chocolate1', 'Yellow', 'Deeppink3', 'Mediumorchid1', 'Darkgreen', 'Blue', 'Gray36', 'Hotpink', 'Yellow4')
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144 unique.samples = unique(PRODF$Sample)
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145
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146 if(length(unique.samples) <= length(naive.colors)){
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147 sample.colors = naive.colors[1:length(unique.samples)]
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148 } else {
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149 sample.colors = rainbow(length(unique.samples))
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150 }
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151
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152 names(sample.colors) = unique.samples
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153
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154 print("Sample.colors")
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155 print(sample.colors)
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5
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156
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157
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158 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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159 write.table(PRODF, "allUnique.txt", sep="\t",quote=F,row.names=F,col.names=T)
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18
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160 #write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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25
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161 write.table(UNPROD, "allUnproductive.txt", sep="\t",quote=F,row.names=F,col.names=T)
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5
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162
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24
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163 print("SAMPLE TABLE:")
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164 print(table(PRODF$Sample))
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165
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5
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166 #write the samples to a file
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167 sampleFile <- file("samples.txt")
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168 un = unique(inputdata$Sample)
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169 un = paste(un, sep="\n")
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170 writeLines(un, sampleFile)
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171 close(sampleFile)
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172
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173 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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174
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175 print("Report Clonality - counting productive/unproductive/unique")
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176
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177 #create the table on the overview page with the productive/unique counts per sample/replicate
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178 #first for sample
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179 sample.count = merge(input.sample.count, prod.sample.count, by="Sample", all.x=T)
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180 sample.count$perc_prod = round(sample.count$Productive / sample.count$All * 100)
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181 sample.count = merge(sample.count, prod.unique.sample.count, by="Sample", all.x=T)
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182 sample.count$perc_prod_un = round(sample.count$Productive_unique / sample.count$All * 100)
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183
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184 sample.count = merge(sample.count , unprod.sample.count, by="Sample", all.x=T)
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185 sample.count$perc_unprod = round(sample.count$Unproductive / sample.count$All * 100)
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186 sample.count = merge(sample.count, unprod.unique.sample.count, by="Sample", all.x=T)
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187 sample.count$perc_unprod_un = round(sample.count$Unproductive_unique / sample.count$All * 100)
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188
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189 #then sample/replicate
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190 rep.count = merge(input.rep.count, prod.rep.count, by=c("Sample", "Replicate"), all.x=T)
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191 rep.count$perc_prod = round(rep.count$Productive / rep.count$All * 100)
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192 rep.count = merge(rep.count, prod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T)
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193 rep.count$perc_prod_un = round(rep.count$Productive_unique / rep.count$All * 100)
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194
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195 rep.count = merge(rep.count, unprod.rep.count, by=c("Sample", "Replicate"), all.x=T)
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196 rep.count$perc_unprod = round(rep.count$Unproductive / rep.count$All * 100)
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197 rep.count = merge(rep.count, unprod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T)
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198 rep.count$perc_unprod_un = round(rep.count$Unproductive_unique / rep.count$All * 100)
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199
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200 rep.count$Sample = paste(rep.count$Sample, rep.count$Replicate, sep="_")
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201 rep.count = rep.count[,names(rep.count) != "Replicate"]
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202
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203 count = rbind(sample.count, rep.count)
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204
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205
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206
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207 write.table(x=count, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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208
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209 # ---------------------- V+J+CDR3 sequence count ----------------------
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210
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211 VJCDR3.count = data.frame(table(clonalityFrame$Top.V.Gene, clonalityFrame$Top.J.Gene, clonalityFrame$CDR3.Seq.DNA))
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212 names(VJCDR3.count) = c("Top.V.Gene", "Top.J.Gene", "CDR3.Seq.DNA", "Count")
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213
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214 VJCDR3.count = VJCDR3.count[VJCDR3.count$Count > 0,]
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215 VJCDR3.count = VJCDR3.count[order(-VJCDR3.count$Count),]
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216
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217 write.table(x=VJCDR3.count, file="VJCDR3_count.txt", sep="\t",quote=F,row.names=F,col.names=T)
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218
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219 # ---------------------- Frequency calculation for V, D and J ----------------------
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220
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221 print("Report Clonality - frequency calculation V, D and J")
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222
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223 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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224 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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225 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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226 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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227
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228 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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229 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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230 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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231 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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232
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233 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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234 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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235 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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236 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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237
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238 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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239
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240 print("Report Clonality - getting genes for species/loci")
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241
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242 Vchain = ""
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243 Dchain = ""
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244 Jchain = ""
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245
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246 if(species == "custom"){
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247 print("Custom genes: ")
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248 splt = unlist(strsplit(locus, ";"))
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249 print(paste("V:", splt[1]))
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250 print(paste("D:", splt[2]))
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251 print(paste("J:", splt[3]))
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252
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253 Vchain = unlist(strsplit(splt[1], ","))
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254 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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255
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256 Dchain = unlist(strsplit(splt[2], ","))
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257 if(length(Dchain) > 0){
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258 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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259 } else {
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260 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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261 }
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262
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263 Jchain = unlist(strsplit(splt[3], ","))
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264 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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265
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266 } else {
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267 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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268
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269 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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270 colnames(Vchain) = c("v.name", "chr.orderV")
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271 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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272 colnames(Dchain) = c("v.name", "chr.orderD")
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273 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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274 colnames(Jchain) = c("v.name", "chr.orderJ")
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275 }
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276 useD = TRUE
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277 if(nrow(Dchain) == 0){
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278 useD = FALSE
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279 cat("No D Genes in this species/locus")
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280 }
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281 print(paste(nrow(Vchain), "genes in V"))
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282 print(paste(nrow(Dchain), "genes in D"))
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283 print(paste(nrow(Jchain), "genes in J"))
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284
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285 # ---------------------- merge with the frequency count ----------------------
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286
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287 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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288
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289 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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290
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291 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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292
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293 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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294
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295 print("Report Clonality - V, D and J frequency plots")
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296
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297 pV = ggplot(PRODFV)
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298 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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8
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299 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage") + scale_fill_manual(values=sample.colors)
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300 pV = pV + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
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18
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301 write.table(x=PRODFV, file="VFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T)
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5
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302
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|
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303 png("VPlot.png",width = 1280, height = 720)
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|
|
304 pV
|
|
32
|
305 dev.off()
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|
|
306
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|
|
307 ggsave("VPlot.pdf", pV, width=13, height=7)
|
|
5
|
308
|
|
|
309 if(useD){
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|
|
310 pD = ggplot(PRODFD)
|
|
|
311 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))
|
|
8
|
312 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage") + scale_fill_manual(values=sample.colors)
|
|
|
313 pD = pD + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
18
|
314 write.table(x=PRODFD, file="DFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
315
|
|
|
316 png("DPlot.png",width = 800, height = 600)
|
|
|
317 print(pD)
|
|
32
|
318 dev.off()
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|
|
319
|
|
|
320 ggsave("DPlot.pdf", pD, width=10, height=7)
|
|
5
|
321 }
|
|
|
322
|
|
|
323 pJ = ggplot(PRODFJ)
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|
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324 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))
|
|
8
|
325 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage") + scale_fill_manual(values=sample.colors)
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326 pJ = pJ + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
18
|
327 write.table(x=PRODFJ, file="JFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
328
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|
|
329 png("JPlot.png",width = 800, height = 600)
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|
330 pJ
|
|
32
|
331 dev.off()
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|
332
|
|
|
333 ggsave("JPlot.pdf", pJ)
|
|
5
|
334
|
|
|
335 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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336
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|
|
337 print("Report Clonality - V, D and J family plots")
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338
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|
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339 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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|
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340 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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341 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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|
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342 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
343 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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|
|
344 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
|
|
|
345 VPlot = ggplot(VGenes)
|
|
|
346 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)) +
|
|
|
347 ggtitle("Distribution of V gene families") +
|
|
8
|
348 ylab("Percentage of sequences") +
|
|
|
349 scale_fill_manual(values=sample.colors) +
|
|
|
350 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
5
|
351 png("VFPlot.png")
|
|
|
352 VPlot
|
|
32
|
353 dev.off()
|
|
|
354 ggsave("VFPlot.pdf", VPlot)
|
|
|
355
|
|
18
|
356 write.table(x=VGenes, file="VFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
357
|
|
|
358 if(useD){
|
|
|
359 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
|
|
|
360 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
|
|
|
361 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
|
|
|
362 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
363 DGenes = merge(DGenes, TotalPerSample, by="Sample")
|
|
|
364 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
|
|
|
365 DPlot = ggplot(DGenes)
|
|
|
366 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)) +
|
|
|
367 ggtitle("Distribution of D gene families") +
|
|
8
|
368 ylab("Percentage of sequences") +
|
|
|
369 scale_fill_manual(values=sample.colors) +
|
|
|
370 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
5
|
371 png("DFPlot.png")
|
|
|
372 print(DPlot)
|
|
32
|
373 dev.off()
|
|
|
374
|
|
|
375 ggsave("DFPlot.pdf", DPlot)
|
|
18
|
376 write.table(x=DGenes, file="DFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
377 }
|
|
|
378
|
|
|
379 # ---------------------- Plotting the cdr3 length ----------------------
|
|
|
380
|
|
|
381 print("Report Clonality - CDR3 length plot")
|
|
|
382
|
|
9
|
383 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length")])
|
|
5
|
384 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
385 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
|
|
|
386 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
|
|
|
387 CDR3LengthPlot = ggplot(CDR3Length)
|
|
15
|
388 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = factor(reorder(CDR3.Length, as.numeric(CDR3.Length))), y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
5
|
389 ggtitle("Length distribution of CDR3") +
|
|
|
390 xlab("CDR3 Length") +
|
|
8
|
391 ylab("Percentage of sequences") +
|
|
|
392 scale_fill_manual(values=sample.colors) +
|
|
|
393 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
5
|
394 png("CDR3LengthPlot.png",width = 1280, height = 720)
|
|
|
395 CDR3LengthPlot
|
|
|
396 dev.off()
|
|
32
|
397
|
|
|
398 ggsave("CDR3LengthPlot.pdf", CDR3LengthPlot, width=12, height=7)
|
|
|
399
|
|
24
|
400 write.table(x=CDR3Length, file="CDR3LengthPlot.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
401
|
|
|
402 # ---------------------- Plot the heatmaps ----------------------
|
|
|
403
|
|
|
404 #get the reverse order for the V and D genes
|
|
|
405 revVchain = Vchain
|
|
|
406 revDchain = Dchain
|
|
|
407 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
408 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
|
|
409
|
|
|
410 if(useD){
|
|
|
411 print("Report Clonality - Heatmaps VD")
|
|
|
412 plotVD <- function(dat){
|
|
|
413 if(length(dat[,1]) == 0){
|
|
|
414 return()
|
|
|
415 }
|
|
|
416
|
|
|
417 img = ggplot() +
|
|
|
418 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
419 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
420 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
421 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
422 xlab("D genes") +
|
|
9
|
423 ylab("V Genes") +
|
|
14
|
424 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro"))
|
|
5
|
425
|
|
|
426 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
427 print(img)
|
|
|
428 dev.off()
|
|
32
|
429
|
|
|
430 ggsave(paste("HeatmapVD_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=13, width=8)
|
|
|
431
|
|
24
|
432 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA)
|
|
5
|
433 }
|
|
|
434
|
|
|
435 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
|
436
|
|
|
437 VandDCount$l = log(VandDCount$Length)
|
|
|
438 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
439 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
440 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
6
|
441 check = is.nan(VandDCount$relLength)
|
|
|
442 if(any(check)){
|
|
|
443 VandDCount[check,"relLength"] = 0
|
|
|
444 }
|
|
5
|
445
|
|
|
446 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name)
|
|
|
447
|
|
|
448 completeVD = merge(VandDCount, cartegianProductVD, by.x=c("Top.V.Gene", "Top.D.Gene"), by.y=c("Top.V.Gene", "Top.D.Gene"), all=TRUE)
|
|
|
449
|
|
|
450 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
451
|
|
|
452 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
453
|
|
|
454 fltr = is.nan(completeVD$relLength)
|
|
|
455 if(all(fltr)){
|
|
|
456 completeVD[fltr,"relLength"] = 0
|
|
|
457 }
|
|
|
458
|
|
|
459 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
460 lapply(VDList, FUN=plotVD)
|
|
|
461 }
|
|
|
462
|
|
|
463 print("Report Clonality - Heatmaps VJ")
|
|
|
464
|
|
|
465 plotVJ <- function(dat){
|
|
|
466 if(length(dat[,1]) == 0){
|
|
|
467 return()
|
|
|
468 }
|
|
|
469 cat(paste(unique(dat[3])[1,1]))
|
|
|
470 img = ggplot() +
|
|
|
471 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
472 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
473 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
474 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
475 xlab("J genes") +
|
|
9
|
476 ylab("V Genes") +
|
|
14
|
477 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro"))
|
|
5
|
478
|
|
|
479 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
480 print(img)
|
|
|
481 dev.off()
|
|
32
|
482
|
|
|
483 ggsave(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=11, width=4)
|
|
|
484
|
|
24
|
485 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA)
|
|
5
|
486 }
|
|
|
487
|
|
|
488 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
489
|
|
|
490 VandJCount$l = log(VandJCount$Length)
|
|
|
491 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
492 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
493 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
494
|
|
6
|
495 check = is.nan(VandJCount$relLength)
|
|
|
496 if(any(check)){
|
|
|
497 VandJCount[check,"relLength"] = 0
|
|
|
498 }
|
|
|
499
|
|
5
|
500 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name)
|
|
|
501
|
|
|
502 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
503 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
504 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
505
|
|
|
506 fltr = is.nan(completeVJ$relLength)
|
|
|
507 if(any(fltr)){
|
|
|
508 completeVJ[fltr,"relLength"] = 1
|
|
|
509 }
|
|
|
510
|
|
|
511 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
512 lapply(VJList, FUN=plotVJ)
|
|
|
513
|
|
|
514
|
|
|
515
|
|
|
516 if(useD){
|
|
|
517 print("Report Clonality - Heatmaps DJ")
|
|
|
518 plotDJ <- function(dat){
|
|
|
519 if(length(dat[,1]) == 0){
|
|
|
520 return()
|
|
|
521 }
|
|
|
522 img = ggplot() +
|
|
|
523 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
524 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
525 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
526 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
527 xlab("J genes") +
|
|
9
|
528 ylab("D Genes") +
|
|
14
|
529 theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro"))
|
|
5
|
530
|
|
|
531 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
532 print(img)
|
|
|
533 dev.off()
|
|
32
|
534
|
|
|
535 ggsave(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, width=4, height=7)
|
|
|
536
|
|
24
|
537 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA)
|
|
5
|
538 }
|
|
|
539
|
|
|
540
|
|
|
541 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
542
|
|
|
543 DandJCount$l = log(DandJCount$Length)
|
|
|
544 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
545 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
546 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
547
|
|
6
|
548 check = is.nan(DandJCount$relLength)
|
|
|
549 if(any(check)){
|
|
|
550 DandJCount[check,"relLength"] = 0
|
|
|
551 }
|
|
|
552
|
|
5
|
553 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name)
|
|
|
554
|
|
|
555 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
556 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
557 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
558
|
|
|
559 fltr = is.nan(completeDJ$relLength)
|
|
|
560 if(any(fltr)){
|
|
|
561 completeDJ[fltr, "relLength"] = 1
|
|
|
562 }
|
|
|
563
|
|
|
564 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
565 lapply(DJList, FUN=plotDJ)
|
|
|
566 }
|
|
|
567
|
|
|
568
|
|
|
569 # ---------------------- output tables for the circos plots ----------------------
|
|
|
570
|
|
|
571 print("Report Clonality - Circos data")
|
|
|
572
|
|
|
573 for(smpl in unique(PRODF$Sample)){
|
|
|
574 PRODF.sample = PRODF[PRODF$Sample == smpl,]
|
|
|
575
|
|
|
576 fltr = PRODF.sample$Top.V.Gene == ""
|
|
|
577 if(any(fltr, na.rm=T)){
|
|
|
578 PRODF.sample[fltr, "Top.V.Gene"] = "NA"
|
|
|
579 }
|
|
|
580
|
|
|
581 fltr = PRODF.sample$Top.D.Gene == ""
|
|
|
582 if(any(fltr, na.rm=T)){
|
|
|
583 PRODF.sample[fltr, "Top.D.Gene"] = "NA"
|
|
|
584 }
|
|
|
585
|
|
|
586 fltr = PRODF.sample$Top.J.Gene == ""
|
|
|
587 if(any(fltr, na.rm=T)){
|
|
|
588 PRODF.sample[fltr, "Top.J.Gene"] = "NA"
|
|
|
589 }
|
|
|
590
|
|
|
591 v.d = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.D.Gene)
|
|
|
592 v.j = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.J.Gene)
|
|
|
593 d.j = table(PRODF.sample$Top.D.Gene, PRODF.sample$Top.J.Gene)
|
|
|
594
|
|
|
595 write.table(v.d, file=paste(smpl, "_VD_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
596 write.table(v.j, file=paste(smpl, "_VJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
597 write.table(d.j, file=paste(smpl, "_DJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
598 }
|
|
|
599
|
|
|
600 # ---------------------- calculating the clonality score ----------------------
|
|
|
601
|
|
|
602 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
603 {
|
|
|
604 print("Report Clonality - Clonality")
|
|
18
|
605 write.table(clonalityFrame, "clonalityComplete.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
606 if(clonality_method == "boyd"){
|
|
|
607 samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
|
|
|
608
|
|
|
609 for (sample in samples){
|
|
|
610 res = data.frame(paste=character(0))
|
|
|
611 sample_id = unique(sample$Sample)[[1]]
|
|
|
612 for(replicate in unique(sample$Replicate)){
|
|
|
613 tmp = sample[sample$Replicate == replicate,]
|
|
|
614 clone_table = data.frame(table(tmp$clonaltype))
|
|
|
615 clone_col_name = paste("V", replicate, sep="")
|
|
|
616 colnames(clone_table) = c("paste", clone_col_name)
|
|
|
617 res = merge(res, clone_table, by="paste", all=T)
|
|
|
618 }
|
|
|
619
|
|
17
|
620 res[is.na(res)] = 0
|
|
|
621
|
|
20
|
622 write.table(res, file=paste("raw_clonality_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F)
|
|
|
623 write.table(as.matrix(res[,2:ncol(res)]), file=paste("raw_clonality2_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F)
|
|
|
624
|
|
|
625 res = read.table(paste("raw_clonality_", sample_id, ".txt", sep=""), header=F, sep="\t", quote="", stringsAsFactors=F, fill=T, comment.char="")
|
|
17
|
626
|
|
5
|
627 infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
|
|
|
628
|
|
13
|
629 #print(infer.result)
|
|
5
|
630
|
|
20
|
631 write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F)
|
|
5
|
632
|
|
|
633 res$type = rowSums(res[,2:ncol(res)])
|
|
|
634
|
|
|
635 coincidence.table = data.frame(table(res$type))
|
|
|
636 colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq")
|
|
20
|
637 write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
638 }
|
|
26
|
639 }
|
|
|
640 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
641
|
|
|
642 #write files for every coincidence group of >1
|
|
|
643 samples = unique(clonalFreq$Sample)
|
|
|
644 for(sample in samples){
|
|
|
645 clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,]
|
|
|
646 if(max(clonalFreqSample$Type) > 1){
|
|
|
647 for(i in 2:max(clonalFreqSample$Type)){
|
|
|
648 clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,]
|
|
|
649 clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,]
|
|
|
650 clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),]
|
|
|
651 write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
652 }
|
|
|
653 }
|
|
|
654 }
|
|
|
655
|
|
|
656 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
657 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
658 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
659 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
660
|
|
|
661 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
662 tcct = textConnection(ct)
|
|
|
663 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
664 close(tcct)
|
|
|
665 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
666 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
667
|
|
|
668 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
|
|
|
669 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
670 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
671 ReplicateReads$Reads = as.numeric(ReplicateReads$Reads)
|
|
|
672 ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads)
|
|
|
673
|
|
|
674 ReplicatePrint <- function(dat){
|
|
|
675 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
676 }
|
|
26
|
677
|
|
|
678 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
679 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
680
|
|
|
681 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
|
682 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
683
|
|
|
684 ReplicateSumPrint <- function(dat){
|
|
|
685 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
686 }
|
|
|
687
|
|
|
688 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
689 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
690
|
|
|
691 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
692 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
693 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
694 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
695 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
696
|
|
|
697 ClonalityScorePrint <- function(dat){
|
|
|
698 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
699 }
|
|
|
700
|
|
|
701 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
702 clonalityScore = unique(clonalityScore)
|
|
|
703
|
|
|
704 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
705 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
706
|
|
|
707 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
708
|
|
|
709
|
|
|
710
|
|
|
711 ClonalityOverviewPrint <- function(dat){
|
|
|
712 dat = dat[order(dat[,2]),]
|
|
|
713 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
714 }
|
|
|
715
|
|
|
716 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
717 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
718
|
|
5
|
719 }
|
|
|
720
|
|
|
721 bak = PRODF
|
|
25
|
722 bakun = UNPROD
|
|
5
|
723
|
|
|
724 imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
|
|
|
725 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
726 {
|
|
|
727 print("found IMGT columns, running junction analysis")
|
|
24
|
728
|
|
5
|
729 #ensure certain columns are in the data (files generated with older versions of IMGT Loader)
|
|
|
730 col.checks = c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb")
|
|
|
731 for(col.check in col.checks){
|
|
|
732 if(!(col.check %in% names(PRODF))){
|
|
|
733 print(paste(col.check, "not found adding new column"))
|
|
|
734 if(nrow(PRODF) > 0){ #because R is anoying...
|
|
|
735 PRODF[,col.check] = 0
|
|
|
736 } else {
|
|
|
737 PRODF = cbind(PRODF, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0)))
|
|
|
738 }
|
|
|
739 if(nrow(UNPROD) > 0){
|
|
|
740 UNPROD[,col.check] = 0
|
|
|
741 } else {
|
|
|
742 UNPROD = cbind(UNPROD, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0)))
|
|
|
743 }
|
|
|
744 }
|
|
|
745 }
|
|
|
746
|
|
24
|
747 PRODF.with.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) > 2,]
|
|
|
748 PRODF.no.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) < 4,]
|
|
26
|
749 write.table(PRODF.no.D, "productive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
|
|
24
|
750
|
|
25
|
751 UNPROD.with.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) > 2,]
|
|
|
752 UNPROD.no.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) < 4,]
|
|
26
|
753 write.table(UNPROD.no.D, "unproductive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
|
|
25
|
754
|
|
5
|
755 num_median = function(x, na.rm=T) { as.numeric(median(x, na.rm=na.rm)) }
|
|
25
|
756
|
|
24
|
757 newData = data.frame(data.table(PRODF.with.D)[,list(unique=.N,
|
|
5
|
758 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
759 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
760 N1=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
761 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
762 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
763 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
764 P3=mean(.SD$P3D.nt.nb, na.rm=T),
|
|
|
765 N2=mean(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
766 P4=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
767 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
768 Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
769 Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
770 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
771 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
5
|
772 by=c("Sample")])
|
|
|
773 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
24
|
774 write.table(newData, "junctionAnalysisProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
775
|
|
24
|
776 newData = data.frame(data.table(PRODF.with.D)[,list(unique=.N,
|
|
5
|
777 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
778 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
|
779 N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
780 P2=num_median(.SD$P5D.nt.nb, na.rm=T),
|
|
|
781 DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
782 DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
783 P3=num_median(.SD$P3D.nt.nb, na.rm=T),
|
|
|
784 N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
785 P4=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
786 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
787 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
788 Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
789 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
790 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
5
|
791 by=c("Sample")])
|
|
|
792 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
24
|
793 write.table(newData, "junctionAnalysisProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
794
|
|
25
|
795 newData = data.frame(data.table(UNPROD.with.D)[,list(unique=.N,
|
|
5
|
796 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
797 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
798 N1=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
799 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
800 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
801 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
802 P3=mean(.SD$P3D.nt.nb, na.rm=T),
|
|
|
803 N2=mean(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
804 P4=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
805 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
806 Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
807 Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
808 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
809 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
5
|
810 by=c("Sample")])
|
|
|
811 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
24
|
812 write.table(newData, "junctionAnalysisUnProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
813
|
|
25
|
814 newData = data.frame(data.table(UNPROD.with.D)[,list(unique=.N,
|
|
5
|
815 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
816 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
|
817 N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
818 P2=num_median(.SD$P5D.nt.nb, na.rm=T),
|
|
|
819 DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
820 DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
821 P3=num_median(.SD$P3D.nt.nb, na.rm=T),
|
|
|
822 N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
823 P4=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
824 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
825 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
826 Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
827 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
828 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
5
|
829 by=c("Sample")])
|
|
24
|
830 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
831 write.table(newData, "junctionAnalysisUnProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
832
|
|
|
833 #---------------- again for no-D
|
|
|
834
|
|
|
835 newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N,
|
|
|
836 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
837 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
26
|
838 N1=mean(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
839 P2=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
840 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
841 Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
26
|
842 Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
843 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
844 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
24
|
845 by=c("Sample")])
|
|
5
|
846 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
24
|
847 write.table(newData, "junctionAnalysisProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
848
|
|
|
849 newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N,
|
|
|
850 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
851 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
30
|
852 N1=num_median(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
853 P2=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
854 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
855 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
30
|
856 Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
857 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
858 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
24
|
859 by=c("Sample")])
|
|
|
860 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
861 write.table(newData, "junctionAnalysisProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
862
|
|
25
|
863 newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N,
|
|
24
|
864 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
865 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
26
|
866 N1=mean(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
867 P2=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
868 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
869 Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
26
|
870 Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
871 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
872 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
24
|
873 by=c("Sample")])
|
|
|
874 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
875 write.table(newData, "junctionAnalysisUnProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
|
876
|
|
26
|
877
|
|
25
|
878 newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N,
|
|
24
|
879 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
880 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
30
|
881 N1=num_median(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
882 P2=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
883 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
884 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
30
|
885 Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T),
|
|
24
|
886 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
38
|
887 Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))),
|
|
24
|
888 by=c("Sample")])
|
|
|
889 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
890 write.table(newData, "junctionAnalysisUnProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
891 }
|
|
|
892
|
|
|
893 PRODF = bak
|
|
25
|
894 UNPROD = bakun
|
|
5
|
895
|
|
|
896
|
|
|
897 # ---------------------- D reading frame ----------------------
|
|
|
898
|
|
8
|
899 D.REGION.reading.frame = PRODF[,c("Sample", "D.REGION.reading.frame")]
|
|
5
|
900
|
|
8
|
901 chck = is.na(D.REGION.reading.frame$D.REGION.reading.frame)
|
|
|
902 if(any(chck)){
|
|
|
903 D.REGION.reading.frame[chck,"D.REGION.reading.frame"] = "No D"
|
|
|
904 }
|
|
5
|
905
|
|
24
|
906 D.REGION.reading.frame.1 = data.frame(data.table(D.REGION.reading.frame)[, list(Freq=.N), by=c("Sample", "D.REGION.reading.frame")])
|
|
|
907
|
|
|
908 D.REGION.reading.frame.2 = data.frame(data.table(D.REGION.reading.frame)[, list(sample.sum=sum(as.numeric(.SD$D.REGION.reading.frame), na.rm=T)), by=c("Sample")])
|
|
5
|
909
|
|
24
|
910 D.REGION.reading.frame = merge(D.REGION.reading.frame.1, D.REGION.reading.frame.2, by="Sample")
|
|
|
911
|
|
|
912 D.REGION.reading.frame$percentage = round(D.REGION.reading.frame$Freq / D.REGION.reading.frame$sample.sum * 100, 1)
|
|
|
913
|
|
|
914 write.table(D.REGION.reading.frame, "DReadingFrame.txt" , sep="\t",quote=F,row.names=F,col.names=T)
|
|
5
|
915
|
|
|
916 D.REGION.reading.frame = ggplot(D.REGION.reading.frame)
|
|
29
|
917 D.REGION.reading.frame = D.REGION.reading.frame + geom_bar(aes( x = D.REGION.reading.frame, y = percentage, fill=Sample), stat='identity', position='dodge' ) + ggtitle("D reading frame") + xlab("Frame") + ylab("Frequency")
|
|
8
|
918 D.REGION.reading.frame = D.REGION.reading.frame + scale_fill_manual(values=sample.colors)
|
|
|
919 D.REGION.reading.frame = D.REGION.reading.frame + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
5
|
920
|
|
|
921 png("DReadingFrame.png")
|
|
|
922 D.REGION.reading.frame
|
|
|
923 dev.off()
|
|
|
924
|
|
32
|
925 ggsave("DReadingFrame.pdf", D.REGION.reading.frame)
|
|
5
|
926
|
|
|
927 # ---------------------- AA composition in CDR3 ----------------------
|
|
|
928
|
|
|
929 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
|
|
|
930
|
|
|
931 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
|
|
|
932
|
|
|
933 AAfreq = list()
|
|
|
934
|
|
|
935 for(i in 1:nrow(TotalPerSample)){
|
|
|
936 sample = TotalPerSample$Sample[i]
|
|
|
937 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
|
|
|
938 AAfreq[[i]]$Sample = sample
|
|
|
939 }
|
|
|
940
|
|
|
941 AAfreq = ldply(AAfreq, data.frame)
|
|
|
942 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
|
|
|
943 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
|
|
|
944
|
|
|
945
|
|
|
946 AAorder = read.table(sep="\t", header=TRUE, text="order.aa\tAA\n1\tR\n2\tK\n3\tN\n4\tD\n5\tQ\n6\tE\n7\tH\n8\tP\n9\tY\n10\tW\n11\tS\n12\tT\n13\tG\n14\tA\n15\tM\n16\tC\n17\tF\n18\tL\n19\tV\n20\tI")
|
|
|
947 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
|
|
|
948
|
|
|
949 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
|
|
|
950
|
|
|
951 AAfreqplot = ggplot(AAfreq)
|
|
|
952 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
|
|
|
953 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
|
954 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
955 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
956 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
8
|
957 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage") + scale_fill_manual(values=sample.colors)
|
|
32
|
958 AAfreqplot = AAfreqplot + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank())
|
|
5
|
959
|
|
|
960 png("AAComposition.png",width = 1280, height = 720)
|
|
|
961 AAfreqplot
|
|
|
962 dev.off()
|
|
32
|
963
|
|
|
964 ggsave("AAComposition.pdf", AAfreqplot, width=12, height=7)
|
|
|
965
|
|
18
|
966 write.table(AAfreq, "AAComposition.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
|
|
5
|
967
|
|
8
|
968 # ---------------------- AA median CDR3 length ----------------------
|
|
5
|
969
|
|
24
|
970 median.aa.l = data.frame(data.table(PRODF)[, list(median=as.double(median(as.numeric(.SD$CDR3.Length, na.rm=T), na.rm=T))), by=c("Sample")])
|
|
|
971 write.table(median.aa.l, "AAMedianBySample.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
|
|
8
|
972
|
|
25
|
973
|
|
|
974 #generate the "Sequences that are present in more than one replicate" dataset
|
|
|
975 clonaltype.in.replicates = inputdata
|
|
38
|
976 clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$Functionality %in% c("productive (see comment)","productive"),]
|
|
37
|
977 clonaltype.in.replicates = na.omit(clonaltype.in.replicates)
|
|
25
|
978 clonaltype = unlist(strsplit(clonaltype, ","))
|
|
37
|
979
|
|
|
980 clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[c(clonaltype, "Replicate")], sep = ":"))
|
|
|
981
|
|
|
982 clonaltype.in.replicates = clonaltype.in.replicates[!duplicated(clonaltype.in.replicates$clonaltype),]
|
|
|
983
|
|
25
|
984 clonaltype = clonaltype[-which(clonaltype == "Sample")]
|
|
|
985
|
|
|
986 clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":"))
|
|
|
987 clonaltype.in.replicates = clonaltype.in.replicates[,c("clonaltype","Replicate", "ID", "Sequence", "Sample")]
|
|
|
988
|
|
|
989 clonaltype.counts = data.frame(table(clonaltype.in.replicates$clonaltype))
|
|
|
990 names(clonaltype.counts) = c("clonaltype", "coincidence")
|
|
|
991
|
|
|
992 clonaltype.counts = clonaltype.counts[clonaltype.counts$coincidence > 1,]
|
|
|
993
|
|
|
994 clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$clonaltype %in% clonaltype.counts$clonaltype,]
|
|
|
995 clonaltype.in.replicates = merge(clonaltype.in.replicates, clonaltype.counts, by="clonaltype")
|
|
37
|
996 clonaltype.in.replicates = clonaltype.in.replicates[order(-clonaltype.in.replicates$coincidence, clonaltype.in.replicates$clonaltype, clonaltype.in.replicates$Replicate),c("coincidence","clonaltype", "Sample", "Replicate", "ID", "Sequence")]
|
|
|
997
|
|
25
|
998
|
|
|
999 write.table(clonaltype.in.replicates, "clonaltypes_replicates.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
|
|
|
1000
|
|
|
1001
|
|
|
1002
|
|
|
1003
|
|
|
1004
|
|
|
1005
|
|
|
1006
|
|
|
1007
|
|
|
1008
|
|
|
1009
|
|
|
1010
|
|
|
1011
|
|
|
1012
|
|
|
1013
|
|
|
1014
|
|
|
1015
|
|
|
1016
|
|
|
1017
|
|
|
1018
|
|
|
1019
|
|
|
1020
|
|
|
1021
|
|
|
1022
|
