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57
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1 library(data.table)
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2 library(ggplot2)
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3
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4 args <- commandArgs(trailingOnly = TRUE)
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5
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6 input = args[1]
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7 genes = unlist(strsplit(args[2], ","))
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8 outputdir = args[3]
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9 print(args[4])
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10 include_fr1 = ifelse(args[4] == "yes", T, F)
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11 setwd(outputdir)
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12
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13 dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F)
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14
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15 if(length(dat$Sequence.ID) == 0){
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16 setwd(outputdir)
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17 result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5))
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18 row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)")
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19 write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F)
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20 transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4))
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21 row.names(transitionTable) = c("A", "C", "G", "T")
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22 transitionTable["A","A"] = NA
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23 transitionTable["C","C"] = NA
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24 transitionTable["G","G"] = NA
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25 transitionTable["T","T"] = NA
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26 write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA)
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27 cat("0", file="n.txt")
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28 stop("No data")
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29 }
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30
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31
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32
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33 cleanup_columns = c("FR1.IMGT.c.a",
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34 "FR2.IMGT.g.t",
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35 "CDR1.IMGT.Nb.of.nucleotides",
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36 "CDR2.IMGT.t.a",
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37 "FR1.IMGT.c.g",
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38 "CDR1.IMGT.c.t",
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39 "FR2.IMGT.a.c",
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40 "FR2.IMGT.Nb.of.mutations",
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41 "FR2.IMGT.g.c",
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42 "FR2.IMGT.a.g",
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43 "FR3.IMGT.t.a",
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44 "FR3.IMGT.t.c",
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45 "FR2.IMGT.g.a",
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46 "FR3.IMGT.c.g",
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47 "FR1.IMGT.Nb.of.mutations",
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48 "CDR1.IMGT.g.a",
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49 "CDR1.IMGT.t.g",
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50 "CDR1.IMGT.g.c",
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51 "CDR2.IMGT.Nb.of.nucleotides",
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52 "FR2.IMGT.a.t",
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53 "CDR1.IMGT.Nb.of.mutations",
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54 "CDR1.IMGT.a.g",
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55 "FR3.IMGT.a.c",
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56 "FR1.IMGT.g.a",
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57 "FR3.IMGT.a.g",
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58 "FR1.IMGT.a.t",
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59 "CDR2.IMGT.a.g",
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60 "CDR2.IMGT.Nb.of.mutations",
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61 "CDR2.IMGT.g.t",
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62 "CDR2.IMGT.a.c",
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63 "CDR1.IMGT.t.c",
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64 "FR3.IMGT.g.c",
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65 "FR1.IMGT.g.t",
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66 "FR3.IMGT.g.t",
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67 "CDR1.IMGT.a.t",
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68 "FR1.IMGT.a.g",
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69 "FR3.IMGT.a.t",
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70 "FR3.IMGT.Nb.of.nucleotides",
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71 "FR2.IMGT.t.c",
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72 "CDR2.IMGT.g.a",
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73 "FR2.IMGT.t.a",
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74 "CDR1.IMGT.t.a",
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75 "FR2.IMGT.t.g",
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76 "FR3.IMGT.t.g",
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77 "FR2.IMGT.Nb.of.nucleotides",
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78 "FR1.IMGT.t.a",
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79 "FR1.IMGT.t.g",
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80 "FR3.IMGT.c.t",
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81 "FR1.IMGT.t.c",
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82 "CDR2.IMGT.a.t",
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83 "FR2.IMGT.c.t",
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84 "CDR1.IMGT.g.t",
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85 "CDR2.IMGT.t.g",
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86 "FR1.IMGT.Nb.of.nucleotides",
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87 "CDR1.IMGT.c.g",
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88 "CDR2.IMGT.t.c",
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89 "FR3.IMGT.g.a",
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90 "CDR1.IMGT.a.c",
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91 "FR2.IMGT.c.a",
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92 "FR3.IMGT.Nb.of.mutations",
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93 "FR2.IMGT.c.g",
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94 "CDR2.IMGT.g.c",
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95 "FR1.IMGT.g.c",
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96 "CDR2.IMGT.c.t",
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97 "FR3.IMGT.c.a",
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98 "CDR1.IMGT.c.a",
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99 "CDR2.IMGT.c.g",
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100 "CDR2.IMGT.c.a",
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101 "FR1.IMGT.c.t",
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102 "FR1.IMGT.Nb.of.silent.mutations",
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103 "FR2.IMGT.Nb.of.silent.mutations",
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104 "FR3.IMGT.Nb.of.silent.mutations",
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105 "FR1.IMGT.Nb.of.nonsilent.mutations",
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106 "FR2.IMGT.Nb.of.nonsilent.mutations",
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107 "FR3.IMGT.Nb.of.nonsilent.mutations")
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108
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109 for(col in cleanup_columns){
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110 dat[,col] = gsub("\\(.*\\)", "", dat[,col])
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111 #dat[dat[,col] == "",] = "0"
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112 dat[,col] = as.numeric(dat[,col])
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113 dat[is.na(dat[,col]),] = 0
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114 }
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115
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116 regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3")
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117 if(!include_fr1){
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118 regions = c("CDR1", "FR2", "CDR2", "FR3")
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119 }
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120
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121 sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) }
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122
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123 VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="")
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124 dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns)
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125
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126 VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="")
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127 dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns)
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128
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129 transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="")
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130 dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns)
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131
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132 transversionMutations_columns = paste(rep(regions, each=8), c(".IMGT.a.c",".IMGT.c.a",".IMGT.a.t",".IMGT.t.a",".IMGT.g.c",".IMGT.c.g",".IMGT.g.t",".IMGT.t.g"), sep="")
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133 dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns)
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134
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135
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136 transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="")
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137 dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns)
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138
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139
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140 totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.c.g",".IMGT.c.t",".IMGT.c.a",".IMGT.g.c",".IMGT.g.a",".IMGT.g.t"), sep="")
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141 #totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.g.a",".IMGT.c.t",".IMGT.c.a",".IMGT.c.g",".IMGT.g.t"), sep="")
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142 dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns)
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143
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144 transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="")
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145 dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns)
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146
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147 totalMutationsAtAT_columns = paste(rep(regions, each=6), c(".IMGT.a.g",".IMGT.a.c",".IMGT.a.t",".IMGT.t.g",".IMGT.t.c",".IMGT.t.a"), sep="")
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148 #totalMutationsAtAT_columns = paste(rep(regions, each=5), c(".IMGT.a.g",".IMGT.t.c",".IMGT.a.c",".IMGT.g.c",".IMGT.t.g"), sep="")
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149 dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns)
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150
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151
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152 FRRegions = regions[grepl("FR", regions)]
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153 CDRRegions = regions[grepl("CDR", regions)]
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154
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155 FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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156 dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns)
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157
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158 CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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159 dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns)
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160
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161 FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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162 dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns)
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163
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164 CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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165 dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns)
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166
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167 mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR")
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168
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169 write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T)
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170
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171 setwd(outputdir)
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172
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173
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174 calculate_result = function(i, gene, dat, matrx, f, fname, name){
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175 tmp = dat[grepl(paste(".*", gene, ".*", sep=""), dat$best_match),]
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176
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177 j = i - 1
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178 x = (j * 3) + 1
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179 y = (j * 3) + 2
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180 z = (j * 3) + 3
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181
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182 if(nrow(tmp) > 0){
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183
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184 matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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185 matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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186 matrx[1,z] = round(matrx[1,x] / matrx[1,y] * 100, digits=1)
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187
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188 matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1)
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189 matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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190 matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1)
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191
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192 matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1)
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193 matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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194 matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1)
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195
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196 matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1)
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197 matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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198 matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1)
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199
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200 matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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201 matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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202 matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1)
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203
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204 matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1)
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205 matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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206 matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1)
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207
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208 matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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209 matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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210 matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1)
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211
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212 matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1)
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213 matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1)
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214 matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1)
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215
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216 matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1)
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217 matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1)
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218 matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1)
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219 }
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220
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221 transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros)
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222 row.names(transitionTable) = c("A", "C", "G", "T")
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223 transitionTable["A","A"] = NA
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224 transitionTable["C","C"] = NA
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225 transitionTable["G","G"] = NA
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226 transitionTable["T","T"] = NA
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227
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228 if(nrow(tmp) > 0){
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229 for(nt1 in nts){
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230 for(nt2 in nts){
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231 if(nt1 == nt2){
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232 next
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233 }
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234 NT1 = LETTERS[letters == nt1]
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235 NT2 = LETTERS[letters == nt2]
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236 FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="")
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237 CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="")
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238 FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="")
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239 CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="")
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240 FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="")
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241 if(include_fr1){
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242 transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)])
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243 } else {
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244 transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)])
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245 }
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246 }
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247 }
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248 }
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249
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250
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251 print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep=""))
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252
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253 write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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254 write.table(x=tmp[,c("Sequence.ID", "best_match", "chunk_hit_percentage", "nt_hit_percentage", "start_locations")], file=paste("matched_", name , "_", fname, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
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255
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256 cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep=""))
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257 cat(length(tmp$Sequence.ID), file=paste(name, "_", fname, "_n.txt" ,sep=""))
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258
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259 matrx
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260 }
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261
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262 nts = c("a", "c", "g", "t")
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263 zeros=rep(0, 4)
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264
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265 funcs = c(median, sum, mean)
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266 fnames = c("median", "sum", "mean")
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267
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268 for(i in 1:length(funcs)){
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269 func = funcs[[i]]
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270 fname = fnames[[i]]
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271
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272 matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=9)
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273
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274 for(i in 1:length(genes)){
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275 matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i])
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276 }
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277
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278 matrx = calculate_result(i + 1, ".*", dat, matrx, func, fname, name="all")
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279
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280 result = data.frame(matrx)
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281 row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)")
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282
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283 write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F)
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284 }
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285
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286
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287 if (!("ggplot2" %in% rownames(installed.packages()))) {
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288 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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289 }
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290
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291
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292 genesForPlot = gsub("[0-9]", "", dat$best_match)
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293 genesForPlot = data.frame(table(genesForPlot))
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294 colnames(genesForPlot) = c("Gene","Freq")
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295 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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296 write.table(genesForPlot, "all.txt", sep="\t",quote=F,row.names=F,col.names=T)
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297
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298
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299 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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300 pc = pc + geom_bar(width = 1, stat = "identity")
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301 pc = pc + coord_polar(theta="y")
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302 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("Classes", "( n =", sum(genesForPlot$Freq), ")"))
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303
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304 png(filename="all.png")
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305 pc
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306 dev.off()
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307
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308
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309 #blegh
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310 genesForPlot = dat[grepl("ca", dat$best_match),]$best_match
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311 if(length(genesForPlot) > 0){
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312 genesForPlot = data.frame(table(genesForPlot))
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313 colnames(genesForPlot) = c("Gene","Freq")
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314 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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315
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316 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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317 pc = pc + geom_bar(width = 1, stat = "identity")
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318 pc = pc + coord_polar(theta="y")
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319 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IgA subclasses", "( n =", sum(genesForPlot$Freq), ")"))
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320 write.table(genesForPlot, "ca.txt", sep="\t",quote=F,row.names=F,col.names=T)
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321
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322 png(filename="ca.png")
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323 print(pc)
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324 dev.off()
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325 }
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326
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327 genesForPlot = dat[grepl("cg", dat$best_match),]$best_match
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328 if(length(genesForPlot) > 0){
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329 genesForPlot = data.frame(table(genesForPlot))
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330 colnames(genesForPlot) = c("Gene","Freq")
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331 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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332
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333 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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334 pc = pc + geom_bar(width = 1, stat = "identity")
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335 pc = pc + coord_polar(theta="y")
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336 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IgG subclasses", "( n =", sum(genesForPlot$Freq), ")"))
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337 write.table(genesForPlot, "cg.txt", sep="\t",quote=F,row.names=F,col.names=T)
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338
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339 png(filename="cg.png")
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340 print(pc)
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341 dev.off()
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342 }
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343
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344 dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2)
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345
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346 p = ggplot(dat, aes(best_match, percentage_mutations))
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347 p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA)
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348 p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot")
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349
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350 png(filename="scatter.png")
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351 print(p)
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352 dev.off()
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353
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354 write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T)
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355
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356 write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T)
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357
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358
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359
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360
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361
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362
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363 dat$best_match_class = substr(dat$best_match, 0, 2)
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364 freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20")
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365 dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels)
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366
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367 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")])
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368
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369 p = ggplot(frequency_bins_data, aes(frequency_bins, frequency_count))
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370 p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge")
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371 p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class")
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372
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373 png(filename="frequency_ranges.png")
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374 print(p)
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375 dev.off()
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376
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377 frequency_bins_data_by_class = frequency_bins_data
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378
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379 write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T)
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380
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381 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "frequency_bins")])
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382
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383 write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T)
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384
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385
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386 #frequency_bins_data_by_class
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387 #frequency_ranges_subclasses.txt
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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