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49
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1 library(data.table)
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2 library(ggplot2)
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3
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0
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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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4
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7 genes = unlist(strsplit(args[2], ","))
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0
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8 outputdir = args[3]
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22
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9 include_fr1 = ifelse(args[4] == "yes", T, F)
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0
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10 setwd(outputdir)
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11
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12 dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F)
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13
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14 if(length(dat$Sequence.ID) == 0){
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4
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15 setwd(outputdir)
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16 result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5))
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17 row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)")
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18 write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F)
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19 transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4))
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20 row.names(transitionTable) = c("A", "C", "G", "T")
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21 transitionTable["A","A"] = NA
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22 transitionTable["C","C"] = NA
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23 transitionTable["G","G"] = NA
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24 transitionTable["T","T"] = NA
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25 write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA)
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26 cat("0", file="n.txt")
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27 stop("No data")
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0
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28 }
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29
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30 cleanup_columns = c("FR1.IMGT.c.a",
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31 "FR2.IMGT.g.t",
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32 "CDR1.IMGT.Nb.of.nucleotides",
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33 "CDR2.IMGT.t.a",
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34 "FR1.IMGT.c.g",
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35 "CDR1.IMGT.c.t",
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36 "FR2.IMGT.a.c",
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37 "FR2.IMGT.Nb.of.mutations",
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38 "FR2.IMGT.g.c",
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39 "FR2.IMGT.a.g",
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40 "FR3.IMGT.t.a",
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41 "FR3.IMGT.t.c",
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42 "FR2.IMGT.g.a",
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43 "FR3.IMGT.c.g",
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44 "FR1.IMGT.Nb.of.mutations",
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45 "CDR1.IMGT.g.a",
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46 "CDR1.IMGT.t.g",
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47 "CDR1.IMGT.g.c",
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48 "CDR2.IMGT.Nb.of.nucleotides",
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49 "FR2.IMGT.a.t",
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50 "CDR1.IMGT.Nb.of.mutations",
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71
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51 "CDR3.IMGT.Nb.of.nucleotides",
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0
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52 "CDR1.IMGT.a.g",
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53 "FR3.IMGT.a.c",
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54 "FR1.IMGT.g.a",
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55 "FR3.IMGT.a.g",
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56 "FR1.IMGT.a.t",
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57 "CDR2.IMGT.a.g",
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58 "CDR2.IMGT.Nb.of.mutations",
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59 "CDR2.IMGT.g.t",
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60 "CDR2.IMGT.a.c",
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61 "CDR1.IMGT.t.c",
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62 "FR3.IMGT.g.c",
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63 "FR1.IMGT.g.t",
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64 "FR3.IMGT.g.t",
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65 "CDR1.IMGT.a.t",
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66 "FR1.IMGT.a.g",
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67 "FR3.IMGT.a.t",
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68 "FR3.IMGT.Nb.of.nucleotides",
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69 "FR2.IMGT.t.c",
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70 "CDR2.IMGT.g.a",
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71 "FR2.IMGT.t.a",
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72 "CDR1.IMGT.t.a",
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73 "FR2.IMGT.t.g",
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74 "FR3.IMGT.t.g",
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75 "FR2.IMGT.Nb.of.nucleotides",
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76 "FR1.IMGT.t.a",
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77 "FR1.IMGT.t.g",
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78 "FR3.IMGT.c.t",
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79 "FR1.IMGT.t.c",
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80 "CDR2.IMGT.a.t",
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81 "FR2.IMGT.c.t",
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82 "CDR1.IMGT.g.t",
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83 "CDR2.IMGT.t.g",
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84 "FR1.IMGT.Nb.of.nucleotides",
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85 "CDR1.IMGT.c.g",
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86 "CDR2.IMGT.t.c",
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87 "FR3.IMGT.g.a",
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88 "CDR1.IMGT.a.c",
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89 "FR2.IMGT.c.a",
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90 "FR3.IMGT.Nb.of.mutations",
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91 "FR2.IMGT.c.g",
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92 "CDR2.IMGT.g.c",
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93 "FR1.IMGT.g.c",
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94 "CDR2.IMGT.c.t",
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95 "FR3.IMGT.c.a",
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96 "CDR1.IMGT.c.a",
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97 "CDR2.IMGT.c.g",
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98 "CDR2.IMGT.c.a",
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42
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99 "FR1.IMGT.c.t",
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100 "FR1.IMGT.Nb.of.silent.mutations",
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101 "FR2.IMGT.Nb.of.silent.mutations",
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102 "FR3.IMGT.Nb.of.silent.mutations",
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103 "FR1.IMGT.Nb.of.nonsilent.mutations",
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104 "FR2.IMGT.Nb.of.nonsilent.mutations",
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105 "FR3.IMGT.Nb.of.nonsilent.mutations")
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0
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106
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107 for(col in cleanup_columns){
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108 dat[,col] = gsub("\\(.*\\)", "", dat[,col])
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109 #dat[dat[,col] == "",] = "0"
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110 dat[,col] = as.numeric(dat[,col])
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73
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111 dat[is.na(dat[,col]),col] = 0
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0
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112 }
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113
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22
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114 regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3")
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115 if(!include_fr1){
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116 regions = c("CDR1", "FR2", "CDR2", "FR3")
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117 }
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0
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118
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22
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119 sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) }
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0
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120
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22
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121 VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="")
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122 dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns)
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123
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124 VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="")
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125 dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns)
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126
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127 transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="")
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128 dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns)
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129
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130 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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131 dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns)
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0
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132
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133
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22
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134 transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="")
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135 dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns)
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0
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136
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49
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137
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138 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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139 #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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22
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140 dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns)
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0
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141
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49
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142 transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="")
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143 dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns)
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144
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145 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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146 #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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147 dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns)
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148
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149
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24
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150 FRRegions = regions[grepl("FR", regions)]
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151 CDRRegions = regions[grepl("CDR", regions)]
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152
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153 FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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154 dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns)
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23
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155
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24
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156 CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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157 dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns)
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158
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159 FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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160 dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns)
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161
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162 CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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163 dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns)
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0
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164
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49
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165 mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR")
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40
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166
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167 write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T)
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0
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168
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4
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169 setwd(outputdir)
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170
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53
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171 calculate_result = function(i, gene, dat, matrx, f, fname, name){
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78
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172 tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),]
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68
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173
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174 j = i - 1
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175 x = (j * 3) + 1
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176 y = (j * 3) + 2
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177 z = (j * 3) + 3
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178
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179 if(nrow(tmp) > 0){
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53
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180
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68
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181 if(fname == "sum"){
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182 matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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183 matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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184 matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1)
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185 } else {
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186 matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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187 matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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188 matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1)
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189 }
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53
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190
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191 matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1)
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192 matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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193 matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1)
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194
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195 matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1)
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196 matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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197 matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1)
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198
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199 matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1)
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200 matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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201 matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1)
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202
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203 matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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204 matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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205 matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1)
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206
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207 matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1)
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208 matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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209 matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1)
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210
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211 matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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212 matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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213 matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1)
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214
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215 matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1)
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216 matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1)
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217 matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1)
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218
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219 matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1)
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220 matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1)
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221 matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1)
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71
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222
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223 if(fname == "sum"){
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224 matrx[10,x] = round(f(rowSums(tmp[,c("FR2.IMGT.Nb.of.nucleotides", "FR3.IMGT.Nb.of.nucleotides")], na.rm=T)), digits=1)
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225 matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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226 matrx[10,z] = round(matrx[10,x] / matrx[10,y], digits=1)
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227
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72
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228 matrx[11,x] = round(f(rowSums(tmp[,c("CDR1.IMGT.Nb.of.nucleotides", "CDR2.IMGT.Nb.of.nucleotides")], na.rm=T)), digits=1)
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71
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229 matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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230 matrx[11,z] = round(matrx[11,x] / matrx[11,y], digits=1)
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231 }
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53
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232 }
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4
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233
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22
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234 transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros)
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4
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235 row.names(transitionTable) = c("A", "C", "G", "T")
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236 transitionTable["A","A"] = NA
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237 transitionTable["C","C"] = NA
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238 transitionTable["G","G"] = NA
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239 transitionTable["T","T"] = NA
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22
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240
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241 if(nrow(tmp) > 0){
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242 for(nt1 in nts){
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243 for(nt2 in nts){
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244 if(nt1 == nt2){
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245 next
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246 }
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247 NT1 = LETTERS[letters == nt1]
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248 NT2 = LETTERS[letters == nt2]
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249 FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="")
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250 CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="")
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251 FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="")
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252 CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="")
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253 FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="")
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254 if(include_fr1){
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255 transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)])
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256 } else {
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257 transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)])
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258 }
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259 }
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260 }
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261 }
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4
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262
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263
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53
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264 print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep=""))
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265
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266 write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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267 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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4
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268
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53
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269 cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep=""))
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78
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270 cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep=""))
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271
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272 print(paste(fname, name, nrow(tmp)))
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53
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273
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274 matrx
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4
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275 }
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276
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53
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277 nts = c("a", "c", "g", "t")
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278 zeros=rep(0, 4)
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49
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279
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53
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280 funcs = c(median, sum, mean)
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281 fnames = c("median", "sum", "mean")
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49
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282
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53
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283 for(i in 1:length(funcs)){
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284 func = funcs[[i]]
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285 fname = fnames[[i]]
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286
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71
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287 rows = 9
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288 if(fname == "sum"){
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289 rows = 11
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290 }
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291 matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows)
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49
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292
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53
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293 for(i in 1:length(genes)){
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294 matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i])
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295 }
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0
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296
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53
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297 matrx = calculate_result(i + 1, ".*", dat, matrx, func, fname, name="all")
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298
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299 result = data.frame(matrx)
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71
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300 if(fname == "sum"){
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301 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)", "nt in FR", "nt in CDR")
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302 } else {
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303 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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304 }
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53
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305
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306 write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F)
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4
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307 }
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308
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309
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310 if (!("ggplot2" %in% rownames(installed.packages()))) {
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311 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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312 }
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49
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313
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4
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314 genesForPlot = gsub("[0-9]", "", dat$best_match)
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315 genesForPlot = data.frame(table(genesForPlot))
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316 colnames(genesForPlot) = c("Gene","Freq")
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317 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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26
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318 write.table(genesForPlot, "all.txt", sep="\t",quote=F,row.names=F,col.names=T)
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319
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4
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320
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321 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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322 pc = pc + geom_bar(width = 1, stat = "identity")
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323 pc = pc + coord_polar(theta="y")
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26
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324 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("Classes", "( n =", sum(genesForPlot$Freq), ")"))
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4
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325
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326 png(filename="all.png")
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327 pc
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328 dev.off()
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329
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330 #blegh
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331 genesForPlot = dat[grepl("ca", dat$best_match),]$best_match
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332 if(length(genesForPlot) > 0){
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333 genesForPlot = data.frame(table(genesForPlot))
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334 colnames(genesForPlot) = c("Gene","Freq")
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335 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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336
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337 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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338 pc = pc + geom_bar(width = 1, stat = "identity")
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339 pc = pc + coord_polar(theta="y")
|
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26
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340 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IgA subclasses", "( n =", sum(genesForPlot$Freq), ")"))
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341 write.table(genesForPlot, "ca.txt", sep="\t",quote=F,row.names=F,col.names=T)
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4
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342
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343 png(filename="ca.png")
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344 print(pc)
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345 dev.off()
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0
|
346 }
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347
|
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4
|
348 genesForPlot = dat[grepl("cg", dat$best_match),]$best_match
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349 if(length(genesForPlot) > 0){
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350 genesForPlot = data.frame(table(genesForPlot))
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351 colnames(genesForPlot) = c("Gene","Freq")
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352 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
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353
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354 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=label))
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355 pc = pc + geom_bar(width = 1, stat = "identity")
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356 pc = pc + coord_polar(theta="y")
|
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26
|
357 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IgG subclasses", "( n =", sum(genesForPlot$Freq), ")"))
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358 write.table(genesForPlot, "cg.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
0
|
359
|
|
4
|
360 png(filename="cg.png")
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361 print(pc)
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|
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362 dev.off()
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|
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363 }
|
|
22
|
364
|
|
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365 dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2)
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366
|
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26
|
367 p = ggplot(dat, aes(best_match, percentage_mutations))
|
|
47
|
368 p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA)
|
|
22
|
369 p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot")
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370
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|
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371 png(filename="scatter.png")
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372 print(p)
|
|
|
373 dev.off()
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|
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374
|
|
49
|
375 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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376
|
|
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377 write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T)
|
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378
|
|
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379
|
|
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380
|
|
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381
|
|
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382
|
|
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383
|
|
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384 dat$best_match_class = substr(dat$best_match, 0, 2)
|
|
|
385 freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20")
|
|
|
386 dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels)
|
|
|
387
|
|
|
388 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")])
|
|
|
389
|
|
|
390 p = ggplot(frequency_bins_data, aes(frequency_bins, frequency_count))
|
|
|
391 p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge")
|
|
|
392 p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class")
|
|
|
393
|
|
|
394 png(filename="frequency_ranges.png")
|
|
|
395 print(p)
|
|
|
396 dev.off()
|
|
|
397
|
|
|
398 frequency_bins_data_by_class = frequency_bins_data
|
|
|
399
|
|
|
400 write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
401
|
|
|
402 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "frequency_bins")])
|
|
|
403
|
|
|
404 write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
405
|
|
|
406
|
|
|
407 #frequency_bins_data_by_class
|
|
|
408 #frequency_ranges_subclasses.txt
|
|
|
409
|
|
22
|
410
|
|
|
411
|
|
|
412
|
|
|
413
|
|
|
414
|
|
|
415
|
|
|
416
|
|
|
417
|
|
|
418
|
|
|
419
|
|
|
420
|
|
|
421
|
|
|
422
|
|
|
423
|
|
|
424
|
|
|
425
|
|
|
426
|
|
|
427
|
|
|
428
|
|
|
429
|
|
|
430
|
|
|
431
|
|
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432
|
|
|
433
|
|
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434
|
|
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435
|
