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1 ########################################################################
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2 # JAMMv1.0.7rev1 is a peak finder for joint analysis of NGS replicates.
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3 # Copyright (C) 2014-2015 Mahmoud Ibrahim
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4 #
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5 # This program is free software: you can redistribute it and/or modify
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6 # it under the terms of the GNU General Public License as published by
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7 # the Free Software Foundation, either version 3 of the License, or
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8 # (at your option) any later version.
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9 #
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10 # This program is distributed in the hope that it will be useful,
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11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
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12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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13 # GNU General Public License for more details.
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14 #
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15 # You should have received a copy of the GNU General Public License
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16 # along with this program. If not, see <http://www.gnu.org/licenses/>.
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17 #
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18 # Contact: mahmoud.ibrahim@mdc-berlin.de
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19 ########################################################################
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20
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21
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22 # =======================
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23 # User-defined variables
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24 # =======================
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25 options(warn = -1, scipen = 1000) #R will not report any warnings (warn = -1), R will not use scientific notation (scipen = 1000)
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26 #=======================> DONE!
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27
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28
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29
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30
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31 # ================================
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32 # Required Libraries check & load
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33 # ================================
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34 if ((is.element('signal', installed.packages()[,1]) == FALSE) || (is.element('parallel', installed.packages()[,1]) == FALSE)) {
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35 stop("R packages 'signal'and 'parallel' are required. Please install them!")
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36 }
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37 suppressPackageStartupMessages(library("signal"))
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38 suppressPackageStartupMessages(library("parallel"))
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39 #=======================> DONE!
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40
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41
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42
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43
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44
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45 # =================
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46 # Custom Functions
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47 # =================
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48 #Get per-row Geometric mean (takes list, returns vectors, not lists!)
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49 geomeanL <- function(mat){
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50 n = length(mat)
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51 if (n > 1) {
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52 mult = (mat[[1]])*(mat[[2]])
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53 if (n > 2) {
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54 for (i in 3:n) {
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55 mult = mult*(mat[[i]])
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56 }
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57 }
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58 mult = mult^(1/n)
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59 mat = mult
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60 return(mat)
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61 } else {
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62 return(mat[[1]])
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63 }
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64 }
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65
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66
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67
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68 #Get per-row Geometric mean (takes matrix, returns vectors, not lists!)
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69 geomean <- function(mat){
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70 n = NCOL(mat)
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71 if (n > 1) {
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72 mult = (mat[,1])*(mat[,2])
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73 if (n > 2) {
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74 for (i in 3:n) {
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75 mult = mult*(mat[,i])
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76 }
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77 }
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78 mult = mult^(1/n)
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79 mat = mult
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80 }
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81 return(mat)
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82 }
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83
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84
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85
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86 #Read in bed(st2) file
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87 parsein = function(bedfile) {
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88 reads = read.table(bedfile, header = FALSE)[[1]]
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89 return(reads)
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90 }
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91
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1
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92 #Read in bedpe(st2)
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93 parseinpe = function(bedfile) {
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94 l = read.table(bedfile, header = FALSE)
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95 l = cbind((l[[1]] + 1), l[[2]])
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96 return(l)
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97 }
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98
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99 #Produces normalized extended read counts (takes output of parsein(), return a vector of floats)
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100 countreads = function(bedfile, reads, frag, chromsize, filelist, chrcount) {
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101
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102 o = which(filelist == bedfile)
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103
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104 counts = vector(mode = "numeric", length = chromsize)
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105
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106 for (j in 1:length(reads[[o]])) {
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107 if ((reads[[o]][j]+frag[o]-1) <= chromsize) {
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108 counts[(reads[[o]][j]):(reads[[o]][j]+frag[o]-1)] = counts[(reads[[o]][j]):(reads[[o]][j]+frag[o]-1)] + 1
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109 }
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110 }
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111
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112 mCount = mean(counts)
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113
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114 if (chrcount == 1) {
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115 counts = counts/mCount
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116 write(paste(mCount), file = paste0(out, "/norma.", o, ".info"))
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117 } else {
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118 meanCounts = mean(as.numeric(read.table(paste0(out, "/norma.", o, ".info"))[[1]]))
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119 if ((mCount > (5*meanCounts)) || (mCount < (0.2*meanCounts))) {
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120 mCount = meanCounts
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121 } else {
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122 write(paste(mCount), file = paste0(out, "/norma.", o, ".info"), append = TRUE)
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123 }
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124 counts = counts/mCount
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125 }
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126
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127 return(counts)
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128 }
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129
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130
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131
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132
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133 #Produces normalized extended read counts (takes output of parsein(), return a vector of floats / PAIRED END)
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134 countreadspe = function(bedfile, reads, chromsize, filelist, chrcount) {
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135
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136 o = which(filelist == bedfile)
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137
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138 counts = vector(mode = "numeric", length = chromsize)
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139
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140 for (j in 1:length(reads[[o]][,1])) {
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141 counts[(reads[[o]][j,1]):(reads[[o]][j,2])] = counts[(reads[[o]][j,1]):(reads[[o]][j,2])] + 1
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142 }
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143
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144 mCount = mean(counts)
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145
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146 if (chrcount == 1) {
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147 counts = counts/mCount
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148 write(paste(mCount), file = paste0(out, "/norma.", o, ".info"))
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149 } else {
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150 meanCounts = mean(as.numeric(read.table(paste0(out, "/norma.", o, ".info"))[[1]]))
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151 if ((mCount > (5*meanCounts)) || (mCount < (0.2*meanCounts))) {
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152 mCount = meanCounts
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153 } else {
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154 write(paste(mCount), file = paste0(out, "/norma.", o, ".info"), append = TRUE)
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155 }
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156 counts = counts/mCount
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157 }
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158
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159 return(counts)
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160 }
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161
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162
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163 #generate signal
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164 getsig = function(winStart, winEnds, startList, stepSize, chromName) {
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165
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166
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167 plz = which(startList == winStart)
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168 winEnd = winEnds[plz]
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169 rWinSizeTemp = winEnd - winStart + 1
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170
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171 #extract subset of the IP
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172 Rs = matrix(nrow = rWinSizeTemp, ncol = numdup)
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173 for (j in 1:numdup) {
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174 Rs[,j] = counts[[j]][winStart:winEnd]
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175 Rs[,j] = filtfilt(rep(1,80)/80,1,Rs[,j])
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176 }
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177
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178 #extract subset of the background and initial signal
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179 if (bkgd != "None") {
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180 Cs = counts[[numdup+1]][winStart:winEnd]
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181 Cs = filtfilt(rep(1,80)/80,1,Cs)
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182 signal = (geomean(Rs)) - (Cs)
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183 } else {
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184 signal = geomean(Rs)
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185 }
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186
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187
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188 #get binned signal
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189 signal = round(colMeans(matrix(c(signal, rep(0, stepSize - length(signal) %% stepSize)), stepSize)), digits = 2)
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190 steps = seq(winStart, winEnd, by = stepSize)
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191 starts = steps - 1
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192 ends = steps + stepSize - 1
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193 if (ends[length(ends)] > winEnd) {
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194 ends[length(ends)] = winEnd
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195 }
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196
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197 #write to file
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198 write(paste(chromName, starts, ends, signal, sep = " "), file = paste0(out, "/", chromName, plz, ".bedSignal"), ncolumns = 1)
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199
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200
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201 return(NULL)
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202 }
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203 #=======================> DONE!
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204
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205
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206
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207
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208
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209 # ==========================
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210 # Parse-in System Variables
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211 # ==========================
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212 args = commandArgs(trailingOnly = TRUE) # Read Arguments from command line
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213
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214 #Parsing arguments and storing values
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215 for (each.arg in args) {
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216 #bed file names
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217 if (grepl('^-bednames=',each.arg)) {
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218 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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219 if (! is.na(arg.split[2]) ) {
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220 bednames <- arg.split[2]
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221 } else {
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222 stop('No bed file names')
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223 }
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224 }
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225 #bed file names
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226 if (grepl('^-chromo=',each.arg)) {
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227 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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228 if (! is.na(arg.split[2]) ) {
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229 chromName <- arg.split[2]
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230 } else {
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231 stop('No bed file names')
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232 }
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233 }
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234 #bed files directory
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235 if (grepl('^-frag=',each.arg)) {
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236 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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237 if (! is.na(arg.split[2]) ) {
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238 frag <- arg.split[2]
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239 }
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240 }
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241 #bakcground files directory
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242 if (grepl('^-bkgd=',each.arg)) {
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243 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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244 if (! is.na(arg.split[2]) ) {
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245 bkgd <- arg.split[2]
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246 } else {
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247 stop('No background file')
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248 }
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249 }
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250 #bakcground files directory
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251 if (grepl('^-out=',each.arg)) {
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252 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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253 if (! is.na(arg.split[2]) ) {
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254 out <- arg.split[2]
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255 } else {
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256 stop('No output directory given')
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257 }
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258 }
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259 #Cluster number
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260 if (grepl('^-clustnummer=',each.arg)) {
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261 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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262 if (! is.na(arg.split[2]) ) {
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263 clustnummer <- as.numeric(arg.split[2])
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264 }
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265 }
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266 #regions
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267 if (grepl('^-regions=',each.arg)) {
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268 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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269 if (! is.na(arg.split[2]) ) {
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270 regions <- arg.split[2]
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271 }
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272 }
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273 #processor cores
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274 if (grepl('^-p=',each.arg)) {
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275 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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276 if (! is.na(arg.split[2]) ) {
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277 cornum <- as.numeric(arg.split[2])
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278 }
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279 }
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280 #chromSize
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281 if (grepl('^-chromoS=',each.arg)) {
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282 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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283 if (! is.na(arg.split[2]) ) {
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284 chromSize <- as.numeric(arg.split[2])
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285 }
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286 }
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287 #stepsize
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288 if (grepl('^-sig=',each.arg)) {
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289 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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290 if (! is.na(arg.split[2]) ) {
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291 sigstep <- as.numeric(arg.split[2])
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292 }
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293 }
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294 #chrcount
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295 if (grepl('^-chrcount=',each.arg)) {
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296 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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297 if (! is.na(arg.split[2]) ) {
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298 chrcount <- as.numeric(arg.split[2])
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299 }
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300 }
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301 #alignment type
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302 if (grepl('^-t=',each.arg)) {
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303 arg.split <- strsplit(each.arg,'=',fixed=TRUE)[[1]]
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304 if (! is.na(arg.split[2]) ) {
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305 type <- arg.split[2]
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306 }
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307 }
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308 }
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309
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310 ##Parse in variables
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311 readsFiles = as.list(strsplit(bednames, ",", fixed = TRUE)[[1]])
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312 numdup = length(readsFiles) #number of replicates
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313 if (bkgd != "None") {
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314 readsFiles[[numdup+1]] = bkgd
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315 }
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316 if (type == "single") {
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317 frags = as.numeric(strsplit(frag, ",", fixed = TRUE)[[1]])
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318 }
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319 rm(bednames)
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320
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321 options(stringsAsFactors = FALSE)
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322 #=======================> DONE!
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323
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324
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325
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326
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327 # =======================
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328 # Some preliminary stuff
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329 # =======================
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330 #import regions
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331 regions = read.table(regions, header = FALSE)
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332 regions = regions[regions[[1]] == chromName, , drop = FALSE]
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333 regions = cbind(regions[[2]] + 1, regions[[3]])
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334
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335 #import read data
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336 if (type == "single") {
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337 if (cornum > 1) {
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338 datain = mclapply(readsFiles, parsein, mc.cores = cornum, mc.preschedule = TRUE) #read in all bed files (samples and control)
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339 } else {
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340 datain = lapply(readsFiles, parsein) #read in all bed files (samples and control)
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341 }
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342 }
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343 #import read data
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344 if (type == "paired") {
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345 if (cornum > 1) {
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346 datain = mclapply(readsFiles, parseinpe, mc.cores = cornum, mc.preschedule = TRUE) #read in all bed files (samples and control)
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347 } else {
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348 datain = lapply(readsFiles, parseinpe) #read in all bed files (samples and control)
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349 }
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0
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350 }
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351 #=======================> DONE!
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352
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353
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354
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355 # ===============
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356 # Counting Reads
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357 # ===============
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1
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358 if (type == "single") {
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359 if (cornum > 1) {
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360 counts = mclapply(readsFiles, countreads, reads = datain, frag = frags, chromsize = chromSize, filelist = readsFiles, chrcount = chrcount, mc.cores = cornum, mc.preschedule = TRUE)
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361 } else {
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362 counts = lapply(readsFiles, countreads, reads = datain, frag = frags, chromsize = chromSize, filelist = readsFiles, chrcount = chrcount)
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363 }
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364 }
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365
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366 if (type == "paired") {
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367 if (cornum > 1) {
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368 counts = mclapply(readsFiles, countreadspe, reads = datain, chromsize = chromSize, filelist = readsFiles, chrcount = chrcount, mc.cores = cornum, mc.preschedule = TRUE)
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369 } else {
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370 counts = lapply(readsFiles, countreadspe, reads = datain, chromsize = chromSize, filelist = readsFiles, chrcount = chrcount)
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371 }
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0
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372 }
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373 rm(datain)
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374 #=======================> DONE!
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375
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376
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377
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378 # ==============
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379 # Getting signal
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380 # ==============
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381 if (cornum > 1) {
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382 sig = mclapply(regions[,1], getsig, winEnds = regions[,2], startList = regions[,1], stepSize = sigstep, chromName, mc.cores = cornum, mc.preschedule = TRUE)
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383 } else {
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384 sig = lapply(regions[,1], getsig, winEnds = regions[,2], startList = regions[,1], stepSize = sigstep, chromName)
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385 }
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386 message("Done!")
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387 #=======================> DONE!
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