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1 # Name: region_motif_compare.r
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2 # Description: Reads in two count files and determines enriched and depleted
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3 # motifs (or any location based feature) based on poisson tests and gc
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4 # corrections. All enrichment ratios relative to overall count / gc ratios.
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5 # Author: Jeremy liu
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6 # Email: jeremy.liu@yale.edu
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7 # Date: 15/02/11
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8 # Note: This script can be invoked with the following command
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9 # R --slave --vanilla -f ./region_motif_compare.r --args <workingdir> <pwm_file>
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10 # <intab1> <intab2> <enriched_tab> <depleted_tab> <plots_png>
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11 # <workingdir> is the directory where plotting.r is saved
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12 # Dependencies: region_motif_data_manager, plotting.r,
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13
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14 # Auxiliary function to concatenate multiple strings
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15 concat <- function(...) {
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16 input_list <- list(...)
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17 return(paste(input_list, sep="", collapse=""))
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18 }
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19
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20 # Supress all warning messages to prevent Galaxy treating warnings as errors
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21 options(warn=-1)
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22
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23 # Set common and data directories
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24 args <- commandArgs()
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25 workingDir = args[7]
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26 pwmFile = unlist(strsplit(args[8], ','))[1] # If duplicate entires, take first one
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27
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28 # Set input and reference files
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29 inTab1 = args[9]
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30 inTab2 = args[10]
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31 enrichTab = args[11]
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32 depleteTab = args[12]
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33 plotsPng = args[13]
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34
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35 # Load dependencies
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36 source(concat(workingDir, "/plotting.r"))
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37
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38 # Auxiliary function to read in tab file and prepare the data
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39 read_tsv <- function(file) {
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40 data = read.table(file, sep="\t", stringsAsFactors=FALSE)
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41 names(data)[names(data) == "V1"] = "motif"
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42 names(data)[names(data) == "V2"] = "counts"
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43 return(data)
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44 }
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45
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46 startTime = Sys.time()
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47 cat("Running ... Started at:", format(startTime, "%a %b %d %X %Y"), "...\n")
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48
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49 # Loading motif position weight matrix (pwm) file
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50 cat("Loading motif postion weight matrices...\n")
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51 lines = scan(pwmFile, what="character", sep="\n", quiet=TRUE)
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52 indices = which(grepl("MOTIF", lines))
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53 names(indices) = lapply(indices, function(i) {
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54 nameline = lines[i]
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55 name = substr(nameline, 7, nchar(nameline))
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56 })
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57
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58 pwms = sapply(indices, function(i) {
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59 infoline = unlist(strsplit(lines[i+1], " "))
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60 alength = as.numeric(infoline[4])
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61 width = as.numeric(infoline[6])
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62 subset = lines[(i+2):(i+2+width-1)]
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63 motiflines = strsplit(subset, " ")
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64 motif = t(do.call(rbind, motiflines))
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65 motif = apply(motif, 2, as.numeric)
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66 }, simplify=FALSE, USE.NAMES=TRUE)
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67
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68 # Loading input tab files
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69 cat("Loading and reading input region motif count files...\n")
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70 region1DF = read_tsv(inTab1)
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71 region2DF = read_tsv(inTab2)
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72 region1Counts = region1DF$counts
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73 region2Counts = region2DF$counts
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74 names(region1Counts) = region1DF$motif
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75 names(region2Counts) = region2DF$motif
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76
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77 # Processing count vectors to account for missing 0 count motifs, then sorting
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78 cat("Performing 0 count correction and sorting...\n")
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79 allNames = union(names(region1Counts), names(region2Counts))
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80 region1Diff = setdiff(allNames, names(region1Counts))
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81 region2Diff = setdiff(allNames, names(region2Counts))
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82 addCounts1 = rep(0, length(region1Diff))
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83 addCounts2 = rep(0, length(region2Diff))
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84 names(addCounts1) = region1Diff
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85 names(addCounts2) = region2Diff
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86 newCounts1 = append(region1Counts, addCounts1)
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87 newCounts2 = append(region2Counts, addCounts2)
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88 region1Counts = newCounts1[sort.int(names(newCounts1), index.return=TRUE)$ix]
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89 region2Counts = newCounts2[sort.int(names(newCounts2), index.return=TRUE)$ix]
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90
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91 # Generate gc content matrix
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92 gc = sapply(pwms, function(i) mean(i[2:3,3:18]))
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93
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94 # Apply poisson test, calculate p and q values, and filter significant results
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95 cat("Applying poisson test...\n")
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96 rValue = sum(region2Counts) / sum(region1Counts)
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97 pValue = sapply(seq(along=region1Counts), function(i) {
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98 poisson.test(c(region1Counts[i], region2Counts[i]), r=1/rValue)$p.value
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99 })
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100 qValue = p.adjust(pValue, "fdr")
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101 indices = which(qValue<0.1 & abs(log2(region1Counts/region2Counts/rValue))>log2(1.5))
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102
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103 # Setting up output diagnostic plots, 4 in 1 png image
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104 png(plotsPng, width=800, height=800)
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105 xlab = "region1_count"
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106 ylab = "region2_count"
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107 lim = c(0.5, 5000)
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108 layout(matrix(1:4, ncol=2))
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109 par(mar=c(5, 5, 5, 1))
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110
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111 # Plot all motif counts along the linear correlation coefficient
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112 plot.scatter(region1Counts+0.5, region2Counts+0.5, log="xy", xlab=xlab, ylab=ylab,
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113 cex.lab=2.2, cex.axis=1.8, xlim=lim, ylim=lim*rValue)
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114 abline(0, rValue, untf=T)
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115 abline(0, rValue*2, untf=T, lty=2)
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116 abline(0, rValue/2, untf=T, lty=2)
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117
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118 # Plot enriched and depleted motifs in red, housed in second plot
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119 plot.scatter(region1Counts+0.5, region2Counts+0.5, log="xy", xlab=xlab, ylab=ylab,
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120 cex.lab=2.2, cex.axis=1.8, xlim=lim, ylim=lim*rValue)
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121 points(region1Counts[indices]+0.5, region2Counts[indices]+0.5, col="red")
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122 abline(0, rValue, untf=T)
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123 abline(0, rValue*2, untf=T, lty=2)
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124 abline(0, rValue/2, untf=T, lty=2)
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125
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126 # Apply and plot gc correction and loess curve
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127 cat("Applying gc correction, rerunning poisson test...\n")
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128 ind = which(region1Counts>5)
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129 gc = gc[names(region2Counts)] # Reorder the indices of pwms to match input data
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130 lo = plot.scatter(gc,log2(region2Counts/region1Counts),draw.loess=T,
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131 xlab="gc content of motif",ylab=paste("log2(",ylab,"/",xlab,")"),
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132 cex.lab=2.2,cex.axis=1.8,ind=ind) # This function is in plotting.r
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133 gcCorrection = 2^approx(lo$loess,xout=gc,rule=2)$y
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134
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135 # Recalculate p and q values, and filter for significant entries
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136 pValueGC = sapply(seq(along=region1Counts),function(i) {
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137 poisson.test(c(region1Counts[i],region2Counts[i]),r=1/gcCorrection[i])$p.value
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138 })
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139 qValueGC=p.adjust(pValueGC,"fdr")
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140 indicesGC = which(qValueGC<0.1 & abs(log2(region1Counts/region2Counts*gcCorrection))>log2(1.5))
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141
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142 # Plot gc corrected motif counts
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143 plot.scatter(region1Counts+0.5, (region2Counts+0.5)/gcCorrection, log="xy",
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144 xlab=xlab, ylab=paste(ylab,"(normalized)"), cex.lab=2.2, cex.axis=1.8,
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145 xlim=lim, ylim=lim)
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146 points(region1Counts[indicesGC]+0.5,
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147 (region2Counts[indicesGC]+0.5)/gcCorrection[indicesGC], col="red")
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148 abline(0,1)
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149 abline(0,1*2,untf=T,lty=2)
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150 abline(0,1/2,untf=T,lty=2)
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151
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152 # Trim results, compile statistics and output to file
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153 # Only does so if significant results are computed
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154 if(length(indicesGC) > 0) {
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155 # Calculate expected counts and enrichment ratios
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156 cat("Calculating statistics...\n")
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157 nullExpect = region1Counts * gcCorrection
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158 enrichment = region2Counts / nullExpect
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159
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160 # Reorder selected indices in ascending pvalue
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161 cat("Reordering by ascending pvalue...\n")
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162 indicesReorder = indicesGC[order(pValueGC[indicesGC])]
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163
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164 # Combine data into one data frame and output to two files
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165 cat("Splitting and outputting data...\n")
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166 outDF = data.frame(motif=names(pValueGC), p=as.numeric(pValueGC), q=qValueGC,
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167 stringsAsFactors=F, region_1_count=region1Counts,
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168 null_expectation=round(nullExpect,2), region_2_count=region2Counts,
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169 enrichment=enrichment)[indicesReorder,]
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170 names(outDF)[which(names(outDF)=="region_1_count")]=xlab
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171 names(outDF)[which(names(outDF)=="region_2_count")]=ylab
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172 indicesEnrich = which(outDF$enrichment>1)
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173 indicesDeplete = which(outDF$enrichment<1)
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174 outDF$enrichment = ifelse(outDF$enrichment>1,
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175 round(outDF$enrichment,3),
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176 paste("1/",round(1/outDF$enrichment,3)))
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177 write.table(outDF[indicesEnrich,], file=enrichTab, quote=FALSE,
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178 sep="\t", append=FALSE, row.names=FALSE, col.names=TRUE)
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179 write.table(outDF[indicesDeplete,], file=depleteTab, quote=FALSE,
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180 sep="\t", append=FALSE, row.names=FALSE, col.names=TRUE)
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181 }
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182
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183 # Catch display messages and output timing information
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184 catchMessage = dev.off()
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185 cat("Done. Job started at:", format(startTime, "%a %b %d %X %Y."),
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186 "Job ended at:", format(Sys.time(), "%a %b %d %X %Y."), "\n")
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