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1
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1 library(data.table)
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2 library(ggplot2)
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3 library(reshape2)
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4
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5 args <- commandArgs(trailingOnly = TRUE)
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6
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7 input = args[1]
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8 genes = unlist(strsplit(args[2], ","))
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9 outputdir = args[3]
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10 empty.region.filter = args[4]
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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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23
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18 row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)")
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1
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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
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27 write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA)
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28 cat("0", file="n.txt")
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29 stop("No data")
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30 }
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31
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32 cleanup_columns = c("FR1.IMGT.c.a",
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33 "FR2.IMGT.g.t",
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34 "CDR1.IMGT.Nb.of.nucleotides",
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35 "CDR2.IMGT.t.a",
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36 "FR1.IMGT.c.g",
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37 "CDR1.IMGT.c.t",
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38 "FR2.IMGT.a.c",
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39 "FR2.IMGT.Nb.of.mutations",
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40 "FR2.IMGT.g.c",
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41 "FR2.IMGT.a.g",
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42 "FR3.IMGT.t.a",
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43 "FR3.IMGT.t.c",
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44 "FR2.IMGT.g.a",
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45 "FR3.IMGT.c.g",
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46 "FR1.IMGT.Nb.of.mutations",
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47 "CDR1.IMGT.g.a",
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48 "CDR1.IMGT.t.g",
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49 "CDR1.IMGT.g.c",
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50 "CDR2.IMGT.Nb.of.nucleotides",
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51 "FR2.IMGT.a.t",
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52 "CDR1.IMGT.Nb.of.mutations",
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53 "CDR3.IMGT.Nb.of.nucleotides",
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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 print("Cleaning up columns")
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110
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111 for(col in cleanup_columns){
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112 dat[,col] = gsub("\\(.*\\)", "", dat[,col])
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113 #dat[dat[,col] == "",] = "0"
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114 dat[,col] = as.numeric(dat[,col])
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115 dat[is.na(dat[,col]),col] = 0
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116 }
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117
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118 regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3")
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119 if(empty.region.filter == "FR1") {
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120 regions = c("CDR1", "FR2", "CDR2", "FR3")
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121 } else if (empty.region.filter == "CDR1") {
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7
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122 regions = c("FR2", "CDR2", "FR3")
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1
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123 } else if (empty.region.filter == "FR2") {
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7
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124 regions = c("CDR2", "FR3")
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1
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125 }
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126
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127 sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) }
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128
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129 print("aggregating data into new columns")
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130
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131 VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="")
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132 dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns)
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133
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134 VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="")
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135 dat$FR3.IMGT.Nb.of.nucleotides = nchar(dat$FR3.IMGT.seq)
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136 dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns)
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137
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138 transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="")
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139 dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns)
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140
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141 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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142 dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns)
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143
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144 transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="")
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145 dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns)
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146
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147 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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148 #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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149 dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns)
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150
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151 transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="")
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152 dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns)
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153
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154 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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155 #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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156 dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns)
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157
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158 FRRegions = regions[grepl("FR", regions)]
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159 CDRRegions = regions[grepl("CDR", regions)]
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160
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161 FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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162 dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns)
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163
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164 CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
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165 dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns)
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166
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167 FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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168 dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns)
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169
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170 CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
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171 dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns)
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172
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173 mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR")
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174 write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T)
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175
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176 setwd(outputdir)
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177
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8
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178 write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T)
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179
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1
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180 base.order = data.frame(base=c("A", "T", "C", "G"), order=1:4)
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181
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182 calculate_result = function(i, gene, dat, matrx, f, fname, name){
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183 tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),]
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184
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185 j = i - 1
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186 x = (j * 3) + 1
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187 y = (j * 3) + 2
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188 z = (j * 3) + 3
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189
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190 if(nrow(tmp) > 0){
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191 if(fname == "sum"){
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192 matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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193 matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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194 matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1)
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195 } else {
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196 matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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197 matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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198 matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1)
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199 }
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200
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201 matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1)
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202 matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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203 matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1)
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204
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205 matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1)
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206 matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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207 matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1)
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208
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209 matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1)
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210 matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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211 matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1)
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212
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213 matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
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214 matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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215 matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1)
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216
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217 matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1)
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218 matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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219 matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1)
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220
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221 matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
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222 matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
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223 matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1)
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224
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225 matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1)
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226 matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1)
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227 matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1)
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228
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229 matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1)
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230 matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1)
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231 matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1)
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232
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233 if(fname == "sum"){
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7
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234
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235 regions.fr = regions[grepl("FR", regions)]
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236 regions.fr = paste(regions.fr, ".IMGT.Nb.of.nucleotides", sep="")
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237 regions.cdr = regions[grepl("CDR", regions)]
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238 regions.cdr = paste(regions.cdr, ".IMGT.Nb.of.nucleotides", sep="")
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239
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240 if(length(regions.fr) > 1){ #in case there is only on FR region (rowSums needs >1 column)
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241 matrx[10,x] = round(f(rowSums(tmp[,regions.fr], na.rm=T)), digits=1)
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242 } else {
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243 matrx[10,x] = round(f(tmp[,regions.fr], na.rm=T), digits=1)
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244 }
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1
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245 matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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246 matrx[10,z] = round(matrx[10,x] / matrx[10,y] * 100, digits=1)
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247
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7
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248 if(length(regions.cdr) > 1){ #in case there is only on CDR region
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249 matrx[11,x] = round(f(rowSums(tmp[,regions.cdr], na.rm=T)), digits=1)
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250 } else {
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251 matrx[11,x] = round(f(tmp[,regions.cdr], na.rm=T), digits=1)
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252 }
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1
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253 matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
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254 matrx[11,z] = round(matrx[11,x] / matrx[11,y] * 100, digits=1)
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255 }
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256 }
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257
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258 transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros)
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259 row.names(transitionTable) = c("A", "C", "G", "T")
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260 transitionTable["A","A"] = NA
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261 transitionTable["C","C"] = NA
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262 transitionTable["G","G"] = NA
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263 transitionTable["T","T"] = NA
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264
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265 if(nrow(tmp) > 0){
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266 for(nt1 in nts){
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267 for(nt2 in nts){
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268 if(nt1 == nt2){
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269 next
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270 }
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271 NT1 = LETTERS[letters == nt1]
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272 NT2 = LETTERS[letters == nt2]
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273 FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="")
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274 CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="")
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275 FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="")
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276 CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="")
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277 FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="")
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278 if (empty.region.filter == "leader"){
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279 transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)])
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280 } else if (empty.region.filter == "FR1") {
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281 transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)])
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282 } else if (empty.region.filter == "CDR1") {
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283 transitionTable[NT1,NT2] = sum(tmp[,c(FR2, CDR2, FR3)])
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284 } else if (empty.region.filter == "FR2") {
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285 transitionTable[NT1,NT2] = sum(tmp[,c(CDR2, FR3)])
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286 }
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287 }
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288 }
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289 transition = transitionTable
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290 transition$id = names(transition)
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291
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292 transition2 = melt(transition, id.vars="id")
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293
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294 transition2 = merge(transition2, base.order, by.x="id", by.y="base")
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295
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296 transition2 = merge(transition2, base.order, by.x="variable", by.y="base")
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297
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298 transition2[is.na(transition2$value),]$value = 0
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23
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299
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4
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300 if(any(transition2$value != 0)){ #having rows of data but a transition table filled with 0 is bad
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23
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301 print("Plotting heatmap and transition")
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302 png(filename=paste("transitions_stacked_", name, ".png", sep=""))
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303 p = ggplot(transition2, aes(factor(reorder(id, order.x)), y=value, fill=factor(reorder(variable, order.y)))) + geom_bar(position="fill", stat="identity", colour="black") #stacked bar
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304 p = p + xlab("From base") + ylab("To base") + ggtitle("Mutations frequency from base to base") + guides(fill=guide_legend(title=NULL))
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5
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305 p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) + scale_fill_manual(values=c("A" = "blue4", "G" = "lightblue1", "C" = "olivedrab3", "T" = "olivedrab4"))
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1
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306 #p = p + scale_colour_manual(values=c("A" = "black", "G" = "black", "C" = "black", "T" = "black"))
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307 print(p)
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308 dev.off()
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309 png(filename=paste("transitions_heatmap_", name, ".png", sep=""))
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310 p = ggplot(transition2, aes(factor(reorder(id, order.x)), factor(reorder(variable, order.y)))) + geom_tile(aes(fill = value)) + scale_fill_gradient(low="white", high="steelblue") #heatmap
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311 p = p + xlab("From base") + ylab("To base") + ggtitle("Mutations frequency from base to base") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=13, colour="black"))
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312 print(p)
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313 dev.off()
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314 } else {
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23
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315 #print("No data to plot")
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1
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316 }
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317 }
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318
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319 #print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep=""))
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320 write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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321 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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322 cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep=""))
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323 cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep=""))
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324 #print(paste(fname, name, nrow(tmp)))
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325 matrx
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326 }
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327 nts = c("a", "c", "g", "t")
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328 zeros=rep(0, 4)
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329 funcs = c(median, sum, mean)
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330 fnames = c("median", "sum", "mean")
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331
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332 print("Creating result tables")
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333
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334 for(i in 1:length(funcs)){
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335 func = funcs[[i]]
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336 fname = fnames[[i]]
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337
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23
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338 print(paste("Creating table for", fname))
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339
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1
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340 rows = 9
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341 if(fname == "sum"){
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342 rows = 11
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343 }
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344 matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows)
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345 for(i in 1:length(genes)){
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346 matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i])
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347 }
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348 matrx = calculate_result(i + 1, ".*", dat[!grepl("unmatched", dat$best_match),], matrx, func, fname, name="all")
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349
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350 result = data.frame(matrx)
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351 if(fname == "sum"){
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23
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352 row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "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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1
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353 } else {
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23
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354 row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)")
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1
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355 }
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356 write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F)
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357 }
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358
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359 print("Adding median number of mutations to sum table")
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360 sum.table = read.table("mutations_sum.txt", sep=",", header=F)
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361 median.table = read.table("mutations_median.txt", sep=",", header=F)
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362
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363 new.table = sum.table[1,]
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364 new.table[2,] = median.table[1,]
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365 new.table[3:12,] = sum.table[2:11,]
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366 new.table[,1] = as.character(new.table[,1])
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367 new.table[2,1] = "Median of Number of Mutations (%)"
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368
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23
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369 #sum.table = sum.table[c("Number of Mutations (%)", "Median of Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "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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1
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370
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371 write.table(x=new.table, file="mutations_sum.txt", sep=",",quote=F,row.names=F,col.names=F)
|
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372
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|
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373 print("Plotting IGA piechart")
|
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374
|
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375 dat = dat[!grepl("^unmatched", dat$best_match),]
|
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376
|
|
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377 #blegh
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378
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379 genesForPlot = dat[grepl("IGA", dat$best_match),]$best_match
|
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380
|
|
|
381 if(length(genesForPlot) > 0){
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|
382 genesForPlot = data.frame(table(genesForPlot))
|
|
|
383 colnames(genesForPlot) = c("Gene","Freq")
|
|
|
384 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
|
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385
|
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|
386 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene))
|
|
|
387 pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGA1" = "lightblue1", "IGA2" = "blue4"))
|
|
|
388 pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL)
|
|
5
|
389 pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank())
|
|
1
|
390 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGA subclasses", "( n =", sum(genesForPlot$Freq), ")"))
|
|
2
|
391 write.table(genesForPlot, "IGA_pie.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
1
|
392
|
|
|
393 png(filename="IGA.png")
|
|
|
394 print(pc)
|
|
|
395 dev.off()
|
|
|
396 }
|
|
|
397
|
|
|
398 print("Plotting IGG piechart")
|
|
|
399
|
|
|
400 genesForPlot = dat[grepl("IGG", dat$best_match),]$best_match
|
|
|
401
|
|
|
402 if(length(genesForPlot) > 0){
|
|
|
403 genesForPlot = data.frame(table(genesForPlot))
|
|
|
404 colnames(genesForPlot) = c("Gene","Freq")
|
|
|
405 genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
|
|
|
406
|
|
|
407 pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene))
|
|
|
408 pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred"))
|
|
|
409 pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL)
|
|
5
|
410 pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank())
|
|
1
|
411 pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGG subclasses", "( n =", sum(genesForPlot$Freq), ")"))
|
|
2
|
412 write.table(genesForPlot, "IGG_pie.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
1
|
413
|
|
|
414 png(filename="IGG.png")
|
|
|
415 print(pc)
|
|
|
416 dev.off()
|
|
|
417 }
|
|
|
418
|
|
|
419 print("Plotting scatterplot")
|
|
|
420
|
|
|
421 dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2)
|
|
|
422 dat.clss = dat
|
|
|
423
|
|
|
424 dat.clss$best_match = substr(dat.clss$best_match, 0, 3)
|
|
|
425
|
|
|
426 dat.clss = rbind(dat, dat.clss)
|
|
|
427
|
|
|
428 p = ggplot(dat.clss, aes(best_match, percentage_mutations))
|
|
|
429 p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA)
|
|
5
|
430 p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"))
|
|
9
|
431 p = p + scale_fill_manual(values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4"))
|
|
|
432 p = p + scale_colour_manual(values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4"))
|
|
1
|
433
|
|
|
434 png(filename="scatter.png")
|
|
|
435 print(p)
|
|
|
436 dev.off()
|
|
|
437
|
|
|
438 write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
439
|
|
|
440 print("Plotting frequency ranges plot")
|
|
|
441
|
|
|
442 dat$best_match_class = substr(dat$best_match, 0, 3)
|
|
|
443 freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20")
|
|
|
444 dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels)
|
|
|
445
|
|
|
446 frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match_class")])
|
|
|
447
|
|
|
448 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")])
|
|
|
449
|
|
|
450 frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class")
|
|
|
451
|
|
|
452 frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2)
|
|
|
453
|
|
|
454 p = ggplot(frequency_bins_data, aes(frequency_bins, frequency))
|
|
5
|
455 p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"))
|
|
9
|
456 p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class") + scale_fill_manual(values=c("IGA" = "blue4", "IGG" = "olivedrab3", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4"))
|
|
1
|
457
|
|
|
458 png(filename="frequency_ranges.png")
|
|
|
459 print(p)
|
|
|
460 dev.off()
|
|
|
461
|
|
|
462 frequency_bins_data_by_class = frequency_bins_data
|
|
|
463
|
|
|
464 write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
465
|
|
|
466 frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "best_match_class", "frequency_bins")])
|
|
|
467
|
|
23
|
468 frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match")])
|
|
|
469
|
|
|
470 frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match")
|
|
1
|
471
|
|
|
472 frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2)
|
|
|
473
|
|
|
474 write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T)
|
|
|
475
|
|
|
476
|
|
|
477
|
|
|
478
|
|
|
479
|
|
|
480
|
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481
|
|
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482
|
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483
|
|
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484
|
|
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485
|
|
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486
|
|
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487
|
|
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488
|
|
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489
|
|
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490
|
|
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491
|
|
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492
|
|
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493
|
|
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494
|
|
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495
|
|
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496
|
|
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497
|
|
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498
|
|
|
499
|
|
|
500
|
|
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501
|
|
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502
|
|
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503
|
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504
|
|
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505
|
|
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506
|
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507
|
|
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508
|
|
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509
|
|
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510
|
|
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511
|
|
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512
|
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513
|
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514
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515
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516
|
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517
|
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518
|
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519
|
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520
|
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521
|
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522
|
|
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523
|
|
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524
|
|
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525
|
|
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526
|
|
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527
|
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528
|
|
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529
|
|
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530
|
