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1 # ----------------------------------------------------------------------#
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2 # Copyright (c) 2011, Richard Lupat & Jason Li.
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3 #
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4 # > Source License <
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5 # This file is part of CONTRA.
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6 #
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7 # CONTRA is free software: you can redistribute it and/or modify
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8 # it under the terms of the GNU General Public License as published by
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9 # the Free Software Foundation, either version 3 of the License, or
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10 # (at your option) any later version.
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11 #
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12 # CONTRA is distributed in the hope that it will be useful,
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13 # but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 # GNU General Public License for more details.
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16 #
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17 # You should have received a copy of the GNU General Public License
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18 # along with CONTRA. If not, see <http://www.gnu.org/licenses/>.
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19 #
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20 #
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21 #-----------------------------------------------------------------------#
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22 # Last Updated : 31 Oct 2011 17:00PM
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23
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24
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25 # Parameters Parsing (from Command Line)
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26 options <- commandArgs(trailingOnly = T)
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27 bins = as.integer(options[1])
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28 rd.cutoff = as.integer(options[2])
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29 min.bases = as.integer(options[3])
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30 outf = options[4]
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31 sample.name = options[5]
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32 plotoption = options[6]
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33 actual.bin = as.numeric(options[7])
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34 min_normal_rd_for_call = as.numeric(options[8])
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35 min_tumour_rd_for_call = as.numeric(options[9])
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36 min_avg_cov_for_call = as.numeric(options[10])
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37
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38 if (sample.name == "No-SampleName")
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39 sample.name = ""
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40
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41 if (sample.name != "")
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42 sample.name = paste(sample.name, ".", sep="")
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43
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44 # Setup output name
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45 out.f = paste(outf, "/table/", sample.name, "CNATable.", rd.cutoff,"rd.", min.bases,"bases.", bins,"bins.txt", sep="")
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46 pdf.out.f = paste(outf, "/plot/", sample.name, "densityplot.", bins, "bins.pdf", sep="")
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47
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48 # Open and read input files
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49 # cnAverageFile = paste("bin", bins, ".txt", sep="")
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50 cnAverageFile = paste(outf,"/buf/bin",bins,".txt",sep="")
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51 boundariesFile = paste(outf,"/buf/bin",bins,".boundaries.txt",sep="")
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52 print (cnAverageFile)
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53 cn.average = read.delim(cnAverageFile, as.is=F, header=F)
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54 cn.boundary= read.delim(boundariesFile,as.is=F, header=F)
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55
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56 # Apply thresholds and data grouping
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57 cn.average.aboveTs = cn.average[cn.average$V3>min.bases,]
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58 cn.average.list = as.matrix(cn.average.aboveTs$V4)
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59
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60 # Get the mean and sd for each bins
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61 cn.average.mean = c()
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62 cn.average.sd = c()
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63 cn.average.log= c()
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64
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65 # Density Plots for each bins
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66 if (plotoption == "True"){
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67 pdf(pdf.out.f)
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68 }
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69 for (j in 1:actual.bin){
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70 cn.average.nth = as.matrix(cn.average.aboveTs[cn.average.aboveTs$V15==j,]$V4)
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71 cn.coverage.nth = as.matrix(cn.average.aboveTs[cn.average.aboveTs$V15==j,]$V11)
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72 boundary.end = cn.boundary[cn.boundary$V1==j,]$V2
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73 boundary.start = cn.boundary[cn.boundary$V1==(j-1),]$V2
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74 boundary.mid = (boundary.end+boundary.start)/2
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75 if (plotoption == "True") {
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76 plot_title = paste("density: bin", bins, sep="")
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77 #plot(density(cn.average.nth),xlim=c(-5,5), title=plot_title)
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78 plot(density(cn.average.nth),xlim=c(-5,5))
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79 }
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80 cn.average.mean = c(cn.average.mean, mean(cn.average.nth))
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81 # cn.average.sd = c(cn.average.sd, sd(cn.average.nth))
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82 cn.average.sd = c(cn.average.sd, apply(cn.average.nth,2,sd))
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83 #cn.average.log = c(cn.average.log, boundary.mid)
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84 cn.average.log = c(cn.average.log, log(mean(cn.coverage.nth),2))
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85 }
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86 if (plotoption == "True"){
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87 dev.off()
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88 }
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89
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90 # for point outside of boundaries
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91 if (bins > 1) {
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92 boundary.first = cn.boundary[cn.boundary$V1==0,]$V2
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93 boundary.last = cn.boundary[cn.boundary$V1==bins,]$V2
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94
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95 b.mean.y2 = cn.average.mean[2]
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96 b.mean.y1 = cn.average.mean[1]
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97 b.sd.y2 = cn.average.sd[2]
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98 b.sd.y1 = cn.average.sd[1]
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99 b.x2 = cn.average.log[2]
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100 b.x1 = cn.average.log[1]
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101
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102 boundary.f.mean = (((b.mean.y2- b.mean.y1)/(b.x2-b.x1))*(boundary.first-b.x1))+b.mean.y1
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103 boundary.f.sd = (((b.sd.y2- b.sd.y1)/(b.x2-b.x1))*(boundary.first-b.x1))+b.sd.y1
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104
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105 if (boundary.f.sd < 0){
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106 boundary.f.sd = 0
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107 }
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108
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109 b.mean.y2 = cn.average.mean[bins]
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110 b.mean.y1 = cn.average.mean[bins-1]
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111 b.sd.y2 = cn.average.sd[bins]
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112 b.sd.y1 = cn.average.sd[bins-1]
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113 b.x2 = cn.average.log[bins]
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114 b.x1 = cn.average.log[bins-1]
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115
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116 boundary.l.mean = (((b.mean.y2- b.mean.y1)/(b.x2-b.x1))*(boundary.last-b.x1))+b.mean.y1
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117 boundary.l.sd = (((b.sd.y2- b.sd.y1)/(b.x2-b.x1))*(boundary.last-b.x1))+b.sd.y1
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118
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119 #cn.average.log = c(boundary.first, cn.average.log, boundary.last)
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120 #cn.linear.mean = c(boundary.f.mean, cn.average.mean, boundary.l.mean)
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121 #cn.linear.sd = c(boundary.f.sd, cn.average.sd, boundary.l.sd)
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122
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123 cn.average.log = c(boundary.first, cn.average.log)
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124 cn.linear.mean = c(boundary.f.mean, cn.average.mean)
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125 cn.linear.sd = c(boundary.f.sd, cn.average.sd)
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126
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127 }
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128
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129 # Linear Interpolation
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130 if (bins > 1 ){
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131 #print(cn.average.log)
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132 #print(cn.linear.mean)
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133 #print(cn.linear.sd)
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134 mean.fn <- approxfun(cn.average.log, cn.linear.mean, rule=2)
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135 sd.fn <- approxfun(cn.average.log, cn.linear.sd, rule=2)
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136 }
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137
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138
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139 # Put the data's details into matrices
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140 ids = as.matrix(cn.average.aboveTs$V1)
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141 exons = as.matrix(cn.average.aboveTs$V6)
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142 exons.pos = as.matrix(cn.average.aboveTs$V5)
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143 gs = as.matrix(cn.average.aboveTs$V2)
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144 number.bases = as.matrix(cn.average.aboveTs$V3)
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145 mean = as.matrix(cn.average.aboveTs$V4)
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146 sd = as.matrix(cn.average.aboveTs$V7)
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147 tumour.rd = as.matrix(cn.average.aboveTs$V8)
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148 tumour.rd.ori = as.matrix(cn.average.aboveTs$V10)
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149 normal.rd = as.matrix(cn.average.aboveTs$V9)
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150 normal.rd.ori = as.matrix(cn.average.aboveTs$V11)
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151 median = as.matrix(cn.average.aboveTs$V12)
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152 MinLogRatio = as.matrix(cn.average.aboveTs$V13)
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153 MaxLogRatio = as.matrix(cn.average.aboveTs$V14)
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154 Bin = as.matrix(cn.average.aboveTs$V15)
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155 Chr = as.matrix(cn.average.aboveTs$V16)
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156 OriStCoordinate = as.matrix(cn.average.aboveTs$V17)
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157 OriEndCoordinate= as.matrix(cn.average.aboveTs$V18)
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158
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159 # Linear Fit
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160 logratios.mean = mean
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161 logcov.mean = log2((normal.rd + tumour.rd)/2)
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162 fit.mean = lm(logratios.mean ~ logcov.mean)
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163 fit.x = fit.mean$coefficient[1]
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164 fit.y = fit.mean$coefficient[2]
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165
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166 adjusted.lr = rep(NA, length(logratios.mean))
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167 for (j in 1:length(logratios.mean)){
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168 fitted.mean = fit.x + fit.y * logcov.mean[j]
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169 adjusted.lr[j] = logratios.mean[j] - fitted.mean
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170 }
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171
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172 fit.mean2 = lm(adjusted.lr ~ logcov.mean)
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173 fit.mean.a = fit.mean2$coefficient[1]
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174 fit.mean.b = fit.mean2$coefficient[2]
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175
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176 fit.mean.fn <- function(x, fit.a, fit.b){
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177 result = fit.a + fit.b * x
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178 return (result)
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179 }
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180
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181 # Adjust SD based on the new adjusted log ratios
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182 logratios.sd = c()
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183 logcov.bins.mean= c()
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184 for (j in 1:actual.bin){
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185 lr.bins.mean = as.matrix(adjusted.lr[cn.average.aboveTs$V15==j])
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186 # logratios.sd = c(logratios.sd, sd(lr.bins.mean))
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187 logratios.sd = c(logratios.sd, apply(lr.bins.mean,2,sd))
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188
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189 cn.coverage.tumour.nth = as.matrix(cn.average.aboveTs[cn.average.aboveTs$V15==j,]$V8)
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190 cn.coverage.normal.nth = as.matrix(cn.average.aboveTs[cn.average.aboveTs$V15==j,]$V9)
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191 cn.coverage.nth = (cn.coverage.tumour.nth + cn.coverage.normal.nth) /2
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192 logcov.bins.mean= c(logcov.bins.mean, log2(mean(cn.coverage.nth)))
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193
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194 }
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195
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196 logratios.sd.ori = logratios.sd
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197 if (length(logratios.sd) > 2) {
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198 logratios.sd = logratios.sd[-length(logratios.sd)]
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199 }
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200
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201 logcov.bins.mean.ori = logcov.bins.mean
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202 if (length(logcov.bins.mean) > 2){
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203 logcov.bins.mean= logcov.bins.mean[-length(logcov.bins.mean)]
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204 }
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205
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206 fit.sd = lm(log2(logratios.sd) ~ logcov.bins.mean)
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207 fit.sd.a = fit.sd$coefficient[1]
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208 fit.sd.b = fit.sd$coefficient[2]
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209
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210 fit.sd.fn <- function(x, fit.a, fit.b){
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211 result = 2 ^ (fit.mean.fn(x, fit.a, fit.b))
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212 return (result)
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213 }
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214
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215 # Get the P Values, called the gain/loss
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216 # with average and sd from each bins
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217 pVal.list = c()
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218 gain.loss = c()
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219
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220 for (i in 1:nrow(cn.average.list)){
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221 #print (i)
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222 #logratio = cn.average.list[i]
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223 #logcov = log(normal.rd.ori[i],2)
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224 logratio = adjusted.lr[i]
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225 logcov = logcov.mean[i]
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226 exon.bin = Bin[i]
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227
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228 if (length(logratios.sd) > 1){
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229 #pVal <- pnorm(logratio, fit.mean.fn(logcov, fit.mean.a, fit.mean.b), fit.sd.fn(logcov, fit.sd.a, fit.sd.b))
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230 pVal <- pnorm(logratio, fit.mean.fn(logcov, fit.mean.a, fit.mean.b), sd.fn(logcov))
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231 } else {
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232 pVal <- pnorm(logratio, 0, logratios.sd[exon.bin])
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233 }
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234
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235 if (pVal > 0.5){
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236 pVal = 1-pVal
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237 gain.loss = c(gain.loss, "gain")
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238 } else {
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239 gain.loss = c(gain.loss, "loss")
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240 }
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241 pVal.list = c(pVal.list, pVal*2)
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242 }
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243
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244 # Get the adjusted P Values
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245 adjusted.pVal.list = p.adjust(pVal.list, method="BH")
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246
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247 # Write the output into a tab-delimited text files
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248 outdf=data.frame(Targeted.Region.ID=ids,Exon.Number=exons,Gene.Sym=gs,Chr, OriStCoordinate, OriEndCoordinate, Mean.of.LogRatio=cn.average.list, Adjusted.Mean.of.LogRatio=adjusted.lr, SD.of.LogRatio=sd, Median.of.LogRatio=median, number.bases, P.Value=pVal.list ,Adjusted.P.Value=adjusted.pVal.list , gain.loss, tumour.rd, normal.rd, tumour.rd.ori, normal.rd.ori, MinLogRatio, MaxLogRatio, BinNumber = Bin)
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249
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250 #min_normal_rd_for_call=5
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251 #min_tumour_rd_for_call=0
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252 #min_avg_cov_for_call=20
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253 outdf$tumour.rd.ori = outdf$tumour.rd.ori-0.5
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254 outdf$normal.rd.ori = outdf$normal.rd.ori-0.5
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255 wh.to.excl = outdf$normal.rd.ori < min_normal_rd_for_call
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256 wh.to.excl = wh.to.excl | outdf$tumour.rd.ori < min_tumour_rd_for_call
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257 wh.to.excl = wh.to.excl | (outdf$tumour.rd.ori+outdf$normal.rd.ori)/2 < min_avg_cov_for_call
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258 outdf$P.Value[wh.to.excl]=NA
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259 outdf$Adjusted.P.Value[wh.to.excl]=NA
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260
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261
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262 write.table(outdf,out.f,sep="\t",quote=F,row.names=F,col.names=T)
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263
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264 #Plotting SD
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265 #a.sd.fn = rep(fit.sd.a, length(logratios.sd.ori))
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266 #b.sd.fn = rep(fit.sd.b, length(logratios.sd.ori))
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267 #sd.after.fit = fit.sd.fn(logcov.bins.mean.ori, fit.sd.a, fit.sd.b)
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268 #sd.out.f = paste(outf, "/plot/", sample.name, "sd.data_fit.", bins, "bins.txt", sep="")
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269 #sd.outdf = data.frame(SD.Before.Fit = logratios.sd.ori, Log.Coverage = logcov.bins.mean.ori, SD.After.Fit = sd.after.fit, a.for.fitting=a.sd.fn, b.for.fitting=b.sd.fn)
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270 #write.table(sd.outdf, sd.out.f,sep="\t", quote=F, row.names=F, col.names=T)
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271
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272
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273 #End of the script
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274 print ("End of cn_analysis.R")
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275 print (i)
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276
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277
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278
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