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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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50 inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="")
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51
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52 print(paste("nrows: ", nrow(inputdata)))
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53
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54 setwd(outdir)
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55
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56 # remove weird rows
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57 inputdata = inputdata[inputdata$Sample != "",]
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58
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59 print(paste("nrows: ", nrow(inputdata)))
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60
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61 #remove the allele from the V,D and J genes
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62 inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
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63 inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
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64 inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
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65
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66 print(paste("nrows: ", nrow(inputdata)))
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67
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68 #filter uniques
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69 inputdata.removed = inputdata[NULL,]
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70
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71 print(paste("nrows: ", nrow(inputdata)))
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72
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73 inputdata$clonaltype = 1:nrow(inputdata)
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74
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75 #keep track of the count of sequences in samples or samples/replicates for the front page overview
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76 input.sample.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample")])
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77 input.rep.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample", "Replicate")])
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78
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79 PRODF = inputdata
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80 UNPROD = inputdata
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81 if(filterproductive){
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82 if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
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83 #PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
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84 PRODF = inputdata[inputdata$Functionality %in% c("productive (see comment)","productive"),]
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85
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86 PRODF.count = data.frame(data.table(PRODF)[, list(count=.N), by=c("Sample")])
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87
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88 UNPROD = inputdata[inputdata$Functionality %in% c("unproductive (see comment)","unproductive"), ]
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89 } else {
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90 PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
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91 UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
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92 }
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93 }
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94
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95 prod.sample.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample")])
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96 prod.rep.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample", "Replicate")])
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97
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98 unprod.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample")])
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99 unprod.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample", "Replicate")])
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100
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101 clonalityFrame = PRODF
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102
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103 #remove duplicates based on the clonaltype
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104 if(clonaltype != "none"){
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105 clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples
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106 PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
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107 PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
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108
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109 UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":"))
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110 UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ]
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111
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112 #again for clonalityFrame but with sample+replicate
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113 clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":"))
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114 clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":"))
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115 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ]
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116 }
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117
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118 print("SAMPLE TABLE:")
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119 print(table(PRODF$Sample))
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120
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121 prod.unique.sample.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample")])
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122 prod.unique.rep.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample", "Replicate")])
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123
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124 unprod.unique.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample")])
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125 unprod.unique.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample", "Replicate")])
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126
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127 PRODF$freq = 1
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128
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129 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
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130 PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
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131 PRODF$freq = gsub("_.*", "", PRODF$freq)
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132 PRODF$freq = as.numeric(PRODF$freq)
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133 if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence
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134 PRODF$freq = 1
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135 }
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136 }
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137
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138
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139
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140 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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141 write.table(PRODF, "allUnique.txt", sep="\t",quote=F,row.names=F,col.names=T)
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142 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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143 write.table(UNPROD, "allUnproductive.csv", sep=",",quote=F,row.names=F,col.names=T)
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144
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145 #write the samples to a file
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146 sampleFile <- file("samples.txt")
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147 un = unique(inputdata$Sample)
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148 un = paste(un, sep="\n")
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149 writeLines(un, sampleFile)
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150 close(sampleFile)
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151
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152 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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153
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154 print("Report Clonality - counting productive/unproductive/unique")
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155
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156 #create the table on the overview page with the productive/unique counts per sample/replicate
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157 #first for sample
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158 sample.count = merge(input.sample.count, prod.sample.count, by="Sample", all.x=T)
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159 sample.count$perc_prod = round(sample.count$Productive / sample.count$All * 100)
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160 sample.count = merge(sample.count, prod.unique.sample.count, by="Sample", all.x=T)
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161 sample.count$perc_prod_un = round(sample.count$Productive_unique / sample.count$All * 100)
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162
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163 sample.count = merge(sample.count , unprod.sample.count, by="Sample", all.x=T)
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164 sample.count$perc_unprod = round(sample.count$Unproductive / sample.count$All * 100)
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165 sample.count = merge(sample.count, unprod.unique.sample.count, by="Sample", all.x=T)
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166 sample.count$perc_unprod_un = round(sample.count$Unproductive_unique / sample.count$All * 100)
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167
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168 #then sample/replicate
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169 rep.count = merge(input.rep.count, prod.rep.count, by=c("Sample", "Replicate"), all.x=T)
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170 rep.count$perc_prod = round(rep.count$Productive / rep.count$All * 100)
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171 rep.count = merge(rep.count, prod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T)
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172 rep.count$perc_prod_un = round(rep.count$Productive_unique / rep.count$All * 100)
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173
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174 rep.count = merge(rep.count, unprod.rep.count, by=c("Sample", "Replicate"), all.x=T)
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175 rep.count$perc_unprod = round(rep.count$Unproductive / rep.count$All * 100)
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176 rep.count = merge(rep.count, unprod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T)
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177 rep.count$perc_unprod_un = round(rep.count$Unproductive_unique / rep.count$All * 100)
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178
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179 rep.count$Sample = paste(rep.count$Sample, rep.count$Replicate, sep="_")
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180 rep.count = rep.count[,names(rep.count) != "Replicate"]
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181
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182 count = rbind(sample.count, rep.count)
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183
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184
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185
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186 write.table(x=count, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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187
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188 # ---------------------- V+J+CDR3 sequence count ----------------------
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189
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190 VJCDR3.count = data.frame(table(clonalityFrame$Top.V.Gene, clonalityFrame$Top.J.Gene, clonalityFrame$CDR3.Seq.DNA))
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191 names(VJCDR3.count) = c("Top.V.Gene", "Top.J.Gene", "CDR3.Seq.DNA", "Count")
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192
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193 VJCDR3.count = VJCDR3.count[VJCDR3.count$Count > 0,]
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194 VJCDR3.count = VJCDR3.count[order(-VJCDR3.count$Count),]
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195
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196 write.table(x=VJCDR3.count, file="VJCDR3_count.txt", sep="\t",quote=F,row.names=F,col.names=T)
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197
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198 # ---------------------- Frequency calculation for V, D and J ----------------------
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199
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200 print("Report Clonality - frequency calculation V, D and J")
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201
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202 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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203 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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204 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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205 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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206
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207 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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208 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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209 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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210 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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211
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212 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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213 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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214 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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215 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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216
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217 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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218
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219 print("Report Clonality - getting genes for species/loci")
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220
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221 Vchain = ""
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222 Dchain = ""
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223 Jchain = ""
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224
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225 if(species == "custom"){
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226 print("Custom genes: ")
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227 splt = unlist(strsplit(locus, ";"))
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228 print(paste("V:", splt[1]))
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229 print(paste("D:", splt[2]))
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230 print(paste("J:", splt[3]))
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231
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232 Vchain = unlist(strsplit(splt[1], ","))
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233 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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234
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235 Dchain = unlist(strsplit(splt[2], ","))
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236 if(length(Dchain) > 0){
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237 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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238 } else {
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239 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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240 }
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241
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242 Jchain = unlist(strsplit(splt[3], ","))
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243 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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244
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245 } else {
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246 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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247
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248 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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249 colnames(Vchain) = c("v.name", "chr.orderV")
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250 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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251 colnames(Dchain) = c("v.name", "chr.orderD")
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252 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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253 colnames(Jchain) = c("v.name", "chr.orderJ")
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254 }
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255 useD = TRUE
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256 if(nrow(Dchain) == 0){
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257 useD = FALSE
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258 cat("No D Genes in this species/locus")
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259 }
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260 print(paste(nrow(Vchain), "genes in V"))
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261 print(paste(nrow(Dchain), "genes in D"))
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262 print(paste(nrow(Jchain), "genes in J"))
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263
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264 # ---------------------- merge with the frequency count ----------------------
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265
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266 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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267
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268 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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269
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270 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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271
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272 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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273
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274 print("Report Clonality - V, D and J frequency plots")
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275
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276 pV = ggplot(PRODFV)
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277 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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278 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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279 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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280
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281 png("VPlot.png",width = 1280, height = 720)
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282 pV
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283 dev.off();
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284
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285 if(useD){
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286 pD = ggplot(PRODFD)
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287 pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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288 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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289 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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290
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291 png("DPlot.png",width = 800, height = 600)
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292 print(pD)
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293 dev.off();
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294 }
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295
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296 pJ = ggplot(PRODFJ)
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297 pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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298 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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299 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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300
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301 png("JPlot.png",width = 800, height = 600)
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302 pJ
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303 dev.off();
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304
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305 pJ = ggplot(PRODFJ)
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306 pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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307 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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308 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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309
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310 png("JPlot.png",width = 800, height = 600)
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311 pJ
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312 dev.off();
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313
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314 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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315
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316 print("Report Clonality - V, D and J family plots")
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317
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318 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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319 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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320 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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321 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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322 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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323 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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324 VPlot = ggplot(VGenes)
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325 VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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326 ggtitle("Distribution of V gene families") +
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327 ylab("Percentage of sequences")
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328 png("VFPlot.png")
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329 VPlot
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330 dev.off();
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331 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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332
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333 if(useD){
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334 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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335 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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336 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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337 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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338 DGenes = merge(DGenes, TotalPerSample, by="Sample")
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339 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
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340 DPlot = ggplot(DGenes)
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341 DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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342 ggtitle("Distribution of D gene families") +
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343 ylab("Percentage of sequences")
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344 png("DFPlot.png")
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345 print(DPlot)
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|
346 dev.off();
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|
347 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
348 }
|
|
|
349
|
|
|
350 # ---------------------- Plotting the cdr3 length ----------------------
|
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|
351
|
|
|
352 print("Report Clonality - CDR3 length plot")
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|
|
353
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|
|
354 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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|
355 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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|
356 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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|
357 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
|
|
|
358 CDR3LengthPlot = ggplot(CDR3Length)
|
|
|
359 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
360 ggtitle("Length distribution of CDR3") +
|
|
|
361 xlab("CDR3 Length") +
|
|
|
362 ylab("Percentage of sequences")
|
|
|
363 png("CDR3LengthPlot.png",width = 1280, height = 720)
|
|
|
364 CDR3LengthPlot
|
|
|
365 dev.off()
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|
|
366 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
367
|
|
|
368 # ---------------------- Plot the heatmaps ----------------------
|
|
|
369
|
|
|
370 #get the reverse order for the V and D genes
|
|
|
371 revVchain = Vchain
|
|
|
372 revDchain = Dchain
|
|
|
373 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
374 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
|
|
375
|
|
|
376 if(useD){
|
|
|
377 print("Report Clonality - Heatmaps VD")
|
|
|
378 plotVD <- function(dat){
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|
|
379 if(length(dat[,1]) == 0){
|
|
|
380 return()
|
|
|
381 }
|
|
|
382
|
|
|
383 img = ggplot() +
|
|
|
384 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
385 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
386 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
387 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
388 xlab("D genes") +
|
|
|
389 ylab("V Genes")
|
|
|
390
|
|
|
391 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
392 print(img)
|
|
|
393 dev.off()
|
|
|
394 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
395 }
|
|
|
396
|
|
|
397 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
|
398
|
|
|
399 VandDCount$l = log(VandDCount$Length)
|
|
|
400 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
401 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
402 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
|
403
|
|
|
404 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name)
|
|
|
405
|
|
|
406 completeVD = merge(VandDCount, cartegianProductVD, by.x=c("Top.V.Gene", "Top.D.Gene"), by.y=c("Top.V.Gene", "Top.D.Gene"), all=TRUE)
|
|
|
407
|
|
|
408 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
409
|
|
|
410 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
411
|
|
|
412 fltr = is.nan(completeVD$relLength)
|
|
|
413 if(all(fltr)){
|
|
|
414 completeVD[fltr,"relLength"] = 0
|
|
|
415 }
|
|
|
416
|
|
|
417 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
418 lapply(VDList, FUN=plotVD)
|
|
|
419 }
|
|
|
420
|
|
|
421 print("Report Clonality - Heatmaps VJ")
|
|
|
422
|
|
|
423 plotVJ <- function(dat){
|
|
|
424 if(length(dat[,1]) == 0){
|
|
|
425 return()
|
|
|
426 }
|
|
|
427 cat(paste(unique(dat[3])[1,1]))
|
|
|
428 img = ggplot() +
|
|
|
429 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
430 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
431 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
432 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
433 xlab("J genes") +
|
|
|
434 ylab("V Genes")
|
|
|
435
|
|
|
436 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
437 print(img)
|
|
|
438 dev.off()
|
|
|
439 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
440 }
|
|
|
441
|
|
|
442 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
443
|
|
|
444 VandJCount$l = log(VandJCount$Length)
|
|
|
445 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
446 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
447 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
448
|
|
|
449 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name)
|
|
|
450
|
|
|
451 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
452 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
453 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
454
|
|
|
455 fltr = is.nan(completeVJ$relLength)
|
|
|
456 if(any(fltr)){
|
|
|
457 completeVJ[fltr,"relLength"] = 1
|
|
|
458 }
|
|
|
459
|
|
|
460 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
461 lapply(VJList, FUN=plotVJ)
|
|
|
462
|
|
|
463
|
|
|
464
|
|
|
465 if(useD){
|
|
|
466 print("Report Clonality - Heatmaps DJ")
|
|
|
467 plotDJ <- function(dat){
|
|
|
468 if(length(dat[,1]) == 0){
|
|
|
469 return()
|
|
|
470 }
|
|
|
471 img = ggplot() +
|
|
|
472 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
473 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
474 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
475 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
476 xlab("J genes") +
|
|
|
477 ylab("D Genes")
|
|
|
478
|
|
|
479 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
480 print(img)
|
|
|
481 dev.off()
|
|
|
482 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
483 }
|
|
|
484
|
|
|
485
|
|
|
486 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
487
|
|
|
488 DandJCount$l = log(DandJCount$Length)
|
|
|
489 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
490 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
491 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
492
|
|
|
493 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name)
|
|
|
494
|
|
|
495 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
496 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
497 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
498
|
|
|
499 fltr = is.nan(completeDJ$relLength)
|
|
|
500 if(any(fltr)){
|
|
|
501 completeDJ[fltr, "relLength"] = 1
|
|
|
502 }
|
|
|
503
|
|
|
504 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
505 lapply(DJList, FUN=plotDJ)
|
|
|
506 }
|
|
|
507
|
|
|
508
|
|
|
509 # ---------------------- output tables for the circos plots ----------------------
|
|
|
510
|
|
|
511 print("Report Clonality - Circos data")
|
|
|
512
|
|
|
513 for(smpl in unique(PRODF$Sample)){
|
|
|
514 PRODF.sample = PRODF[PRODF$Sample == smpl,]
|
|
|
515
|
|
|
516 fltr = PRODF.sample$Top.V.Gene == ""
|
|
|
517 if(any(fltr, na.rm=T)){
|
|
|
518 PRODF.sample[fltr, "Top.V.Gene"] = "NA"
|
|
|
519 }
|
|
|
520
|
|
|
521 fltr = PRODF.sample$Top.D.Gene == ""
|
|
|
522 if(any(fltr, na.rm=T)){
|
|
|
523 PRODF.sample[fltr, "Top.D.Gene"] = "NA"
|
|
|
524 }
|
|
|
525
|
|
|
526 fltr = PRODF.sample$Top.J.Gene == ""
|
|
|
527 if(any(fltr, na.rm=T)){
|
|
|
528 PRODF.sample[fltr, "Top.J.Gene"] = "NA"
|
|
|
529 }
|
|
|
530
|
|
|
531 v.d = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.D.Gene)
|
|
|
532 v.j = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.J.Gene)
|
|
|
533 d.j = table(PRODF.sample$Top.D.Gene, PRODF.sample$Top.J.Gene)
|
|
|
534
|
|
|
535 write.table(v.d, file=paste(smpl, "_VD_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
536 write.table(v.j, file=paste(smpl, "_VJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
537 write.table(d.j, file=paste(smpl, "_DJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA)
|
|
|
538 }
|
|
|
539
|
|
|
540 # ---------------------- calculating the clonality score ----------------------
|
|
|
541
|
|
|
542 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
543 {
|
|
|
544 print("Report Clonality - Clonality")
|
|
|
545 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
546 if(clonality_method == "boyd"){
|
|
|
547 samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
|
|
|
548
|
|
|
549 for (sample in samples){
|
|
|
550 res = data.frame(paste=character(0))
|
|
|
551 sample_id = unique(sample$Sample)[[1]]
|
|
|
552 for(replicate in unique(sample$Replicate)){
|
|
|
553 tmp = sample[sample$Replicate == replicate,]
|
|
|
554 clone_table = data.frame(table(tmp$clonaltype))
|
|
|
555 clone_col_name = paste("V", replicate, sep="")
|
|
|
556 colnames(clone_table) = c("paste", clone_col_name)
|
|
|
557 res = merge(res, clone_table, by="paste", all=T)
|
|
|
558 }
|
|
|
559
|
|
|
560 res[is.na(res)] = 0
|
|
|
561 infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
|
|
|
562
|
|
|
563 print(infer.result)
|
|
|
564
|
|
|
565 write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F)
|
|
|
566
|
|
|
567 res$type = rowSums(res[,2:ncol(res)])
|
|
|
568
|
|
|
569 coincidence.table = data.frame(table(res$type))
|
|
|
570 colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq")
|
|
|
571 write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
572 }
|
|
|
573 } else {
|
|
|
574 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
575
|
|
|
576 #write files for every coincidence group of >1
|
|
|
577 samples = unique(clonalFreq$Sample)
|
|
|
578 for(sample in samples){
|
|
|
579 clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,]
|
|
|
580 if(max(clonalFreqSample$Type) > 1){
|
|
|
581 for(i in 2:max(clonalFreqSample$Type)){
|
|
|
582 clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,]
|
|
|
583 clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,]
|
|
|
584 clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),]
|
|
|
585 write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
586 }
|
|
|
587 }
|
|
|
588 }
|
|
|
589
|
|
|
590 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
591 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
592 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
593 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
594
|
|
|
595 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
596 tcct = textConnection(ct)
|
|
|
597 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
598 close(tcct)
|
|
|
599 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
600 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
601
|
|
|
602 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
|
|
|
603 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
604 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
605 ReplicateReads$Reads = as.numeric(ReplicateReads$Reads)
|
|
|
606 ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads)
|
|
|
607
|
|
|
608 ReplicatePrint <- function(dat){
|
|
|
609 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
610 }
|
|
|
611
|
|
|
612 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
613 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
614
|
|
|
615 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
|
616 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
617
|
|
|
618 ReplicateSumPrint <- function(dat){
|
|
|
619 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
620 }
|
|
|
621
|
|
|
622 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
623 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
624
|
|
|
625 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
626 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
627 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
628 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
629 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
630
|
|
|
631 ClonalityScorePrint <- function(dat){
|
|
|
632 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
633 }
|
|
|
634
|
|
|
635 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
636 clonalityScore = unique(clonalityScore)
|
|
|
637
|
|
|
638 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
639 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
640
|
|
|
641 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
642
|
|
|
643
|
|
|
644
|
|
|
645 ClonalityOverviewPrint <- function(dat){
|
|
|
646 dat = dat[order(dat[,2]),]
|
|
|
647 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
648 }
|
|
|
649
|
|
|
650 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
651 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
652 }
|
|
|
653 }
|
|
|
654
|
|
|
655 bak = PRODF
|
|
|
656
|
|
|
657 imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
|
|
|
658 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
659 {
|
|
|
660 print("found IMGT columns, running junction analysis")
|
|
|
661
|
|
|
662 if(locus %in% c("IGK","IGL", "TRA", "TRG")){
|
|
|
663 print("VJ recombination, no filtering on absent D")
|
|
|
664 } else {
|
|
|
665 print("VDJ recombination, using N column for junction analysis")
|
|
|
666 fltr = nchar(PRODF$Top.D.Gene) < 4
|
|
|
667 print(paste("Removing", sum(fltr), "sequences without a identified D"))
|
|
|
668 PRODF = PRODF[!fltr,]
|
|
|
669 }
|
|
|
670
|
|
|
671
|
|
|
672 #ensure certain columns are in the data (files generated with older versions of IMGT Loader)
|
|
|
673 col.checks = c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb")
|
|
|
674 for(col.check in col.checks){
|
|
|
675 if(!(col.check %in% names(PRODF))){
|
|
|
676 print(paste(col.check, "not found adding new column"))
|
|
|
677 if(nrow(PRODF) > 0){ #because R is anoying...
|
|
|
678 PRODF[,col.check] = 0
|
|
|
679 } else {
|
|
|
680 PRODF = cbind(PRODF, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0)))
|
|
|
681 }
|
|
|
682 if(nrow(UNPROD) > 0){
|
|
|
683 UNPROD[,col.check] = 0
|
|
|
684 } else {
|
|
|
685 UNPROD = cbind(UNPROD, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0)))
|
|
|
686 }
|
|
|
687 }
|
|
|
688 }
|
|
|
689
|
|
|
690 num_median = function(x, na.rm=T) { as.numeric(median(x, na.rm=na.rm)) }
|
|
|
691
|
|
|
692 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
|
693 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
694 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
695 N1=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
696 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
697 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
698 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
699 P3=mean(.SD$P3D.nt.nb, na.rm=T),
|
|
|
700 N2=mean(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
701 P4=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
702 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
703 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)),
|
|
|
704 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)),
|
|
|
705 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
|
706 Median.CDR3.l=median(.SD$CDR3.Length.DNA)),
|
|
|
707 by=c("Sample")])
|
|
|
708 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
709 write.table(newData, "junctionAnalysisProd_mean.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
710
|
|
|
711 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
|
712 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
713 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
|
714 N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
715 P2=num_median(.SD$P5D.nt.nb, na.rm=T),
|
|
|
716 DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
717 DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
718 P3=num_median(.SD$P3D.nt.nb, na.rm=T),
|
|
|
719 N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
720 P4=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
721 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
722 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
723 Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
724 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
|
725 Median.CDR3.l=median(.SD$CDR3.Length.DNA)),
|
|
|
726 by=c("Sample")])
|
|
|
727 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
728 write.table(newData, "junctionAnalysisProd_median.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
729
|
|
|
730 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
|
|
|
731 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
732 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
733 N1=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
734 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
735 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
736 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
737 P3=mean(.SD$P3D.nt.nb, na.rm=T),
|
|
|
738 N2=mean(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
739 P4=mean(.SD$P5J.nt.nb, na.rm=T),
|
|
|
740 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
741 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)),
|
|
|
742 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)),
|
|
|
743 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
|
744 Median.CDR3.l=median(.SD$CDR3.Length.DNA)),
|
|
|
745 by=c("Sample")])
|
|
|
746 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
747 write.table(newData, "junctionAnalysisUnProd_mean.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
748
|
|
|
749 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
|
|
|
750 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
751 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
|
|
|
752 N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
753 P2=num_median(.SD$P5D.nt.nb, na.rm=T),
|
|
|
754 DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
755 DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
756 P3=num_median(.SD$P3D.nt.nb, na.rm=T),
|
|
|
757 N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
758 P4=num_median(.SD$P5J.nt.nb, na.rm=T),
|
|
|
759 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
760 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
|
|
|
761 Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)),
|
|
|
762 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
|
|
|
763 Median.CDR3.l=median(.SD$CDR3.Length.DNA)),
|
|
|
764 by=c("Sample")])
|
|
|
765
|
|
|
766 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
|
|
767 write.table(newData, "junctionAnalysisUnProd_median.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
768 }
|
|
|
769
|
|
|
770 PRODF = bak
|
|
|
771
|
|
|
772
|
|
|
773 # ---------------------- D reading frame ----------------------
|
|
|
774
|
|
|
775 D.REGION.reading.frame = PRODF$D.REGION.reading.frame
|
|
|
776
|
|
|
777 D.REGION.reading.frame[is.na(D.REGION.reading.frame)] = "No D"
|
|
|
778
|
|
|
779 D.REGION.reading.frame = data.frame(table(D.REGION.reading.frame))
|
|
|
780
|
|
|
781 write.table(D.REGION.reading.frame, "DReadingFrame.csv" , sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
782
|
|
|
783 D.REGION.reading.frame = ggplot(D.REGION.reading.frame)
|
|
|
784 D.REGION.reading.frame = D.REGION.reading.frame + geom_bar(aes( x = D.REGION.reading.frame, y = Freq), stat='identity', position='dodge' ) + ggtitle("D reading frame") + xlab("Frequency") + ylab("Frame")
|
|
|
785
|
|
|
786 png("DReadingFrame.png")
|
|
|
787 D.REGION.reading.frame
|
|
|
788 dev.off()
|
|
|
789
|
|
|
790
|
|
|
791
|
|
|
792
|
|
|
793 # ---------------------- AA composition in CDR3 ----------------------
|
|
|
794
|
|
|
795 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
|
|
|
796
|
|
|
797 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
|
|
|
798
|
|
|
799 AAfreq = list()
|
|
|
800
|
|
|
801 for(i in 1:nrow(TotalPerSample)){
|
|
|
802 sample = TotalPerSample$Sample[i]
|
|
|
803 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
|
|
|
804 AAfreq[[i]]$Sample = sample
|
|
|
805 }
|
|
|
806
|
|
|
807 AAfreq = ldply(AAfreq, data.frame)
|
|
|
808 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
|
|
|
809 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
|
|
|
810
|
|
|
811
|
|
|
812 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")
|
|
|
813 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
|
|
|
814
|
|
|
815 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
|
|
|
816
|
|
|
817 AAfreqplot = ggplot(AAfreq)
|
|
|
818 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
|
|
|
819 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
|
820 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
821 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
822 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
|
823 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
|
|
|
824
|
|
|
825 png("AAComposition.png",width = 1280, height = 720)
|
|
|
826 AAfreqplot
|
|
|
827 dev.off()
|
|
|
828 write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
|
|
|
829
|
|
|
830
|
