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1 #!/bin/perl
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2
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3 #EdgeR.pl Version 0.0.3
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4 #Contributors: Monica Britton, Blythe Durbin-Johnson, Joseph Fass, Nikhil Joshi, Alex Mawla
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5
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6 use strict;
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7 use warnings;
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8 use Getopt::Std;
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9 use File::Basename;
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10 use File::Path qw(make_path remove_tree);
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11
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12 $| = 1;
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13
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14 my %OPTIONS = (a => "glm", d => "tag", f => "BH", r => 5, u => "movingave");
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15
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16 getopts('a:d:e:f:h:lmn:o:r:tu:', \%OPTIONS);
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17
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18
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19 die qq(
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20 Usage: edgeR.pl [OPTIONS] factor::factor1::levels [factor::factor2::levels ...] cp::cont_pred1::values [cp::cont_pred2::values ...] cnt::contrast1 [cnt::contrast2] matrix
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21
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22 OPTIONS: -a STR Type Of Analysis [glm, pw, limma] (default: $OPTIONS{a})
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23 -d STR The dispersion estimate to use for GLM analysis [tag] (default: $OPTIONS{d})
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24 -e STR Path to place additional output files
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25 -f STR False discovery rate adjustment method [BH] (default: $OPTIONS{f})
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26 -h STR Name of html file for additional files
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27 -l Output the normalised digital gene expression matrix in log2 format (only applicable when using limma and -n is also specified)
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28 -m Perform all pairwise comparisons
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29 -n STR File name to output the normalised digital gene expression matrix (only applicable when usinf glm or limma model)
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30 -o STR File name to output csv file with results
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31 -r INT Common Dispersion Rowsum Filter, ony applicable when 1 factor analysis selected (default: $OPTIONS{r})
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32 -t Estimate Tagwise Disp when performing 1 factor analysis
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33 -u STR Method for allowing the prior distribution for the dispersion to be abundance- dependent ["movingave"] (default: $OPTIONS{u})
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34
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35 ) if(!@ARGV);
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36
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37 my $matrix = pop @ARGV;
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38
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39 make_path($OPTIONS{e});
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40 open(Rcmd,">$OPTIONS{e}/r_script.R") or die "Cannot open $OPTIONS{e}/r_script.R\n\n";
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41 print Rcmd "
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42 zz <- file(\"$OPTIONS{e}/r_script.err\", open=\"wt\")
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43 sink(zz)
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44 sink(zz, type=\"message\")
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45
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46 library(edgeR)
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47 library(limma)
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48
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49 toc <- read.table(\"$matrix\", sep=\"\\t\", comment=\"\", as.is=T)
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50 groups <- sapply(toc[1, -1], strsplit, \":\")
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51 for(i in 1:length(groups)) { g <- make.names(groups[[i]][2]); names(groups)[i] <- g; groups[[i]] <- groups[[i]][-2] }
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52 colnames(toc) <- make.names(toc[2,])
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53 toc[,1] <- gsub(\",\", \".\", toc[,1])
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54 tagnames <- toc[-(1:2), 1]
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55 rownames(toc) <- toc[,1]
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56 toc <- toc[-(1:2), -1]
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57 for(i in colnames(toc)) toc[, i] <- as.numeric(toc[,i])
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58 norm_factors <- calcNormFactors(as.matrix(toc))
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59
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60 pw_tests <- list()
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61 uniq_groups <- unique(names(groups))
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62 for(i in 1:(length(uniq_groups)-1)) for(j in (i+1):length(uniq_groups)) pw_tests[[length(pw_tests)+1]] <- c(uniq_groups[i], uniq_groups[j])
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63 DGE <- DGEList(toc, lib.size=norm_factors*colSums(toc), group=names(groups))
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64 pdf(\"$OPTIONS{e}/MA_plots_normalisation.pdf\", width=14)
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65 for(i in 1:length(pw_tests)) {
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66 j <- c(which(names(groups) == pw_tests[[i]][1])[1], which(names(groups) == pw_tests[[i]][2])[1])
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67 par(mfrow = c(1, 2))
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68 maPlot(toc[, j[1]], toc[, j[2]], normalize = TRUE, pch = 19, cex = 0.2, ylim = c(-10, 10), main=paste(\"MA Plot\", colnames(toc)[j[1]], \"vs\", colnames(toc)[j[2]]))
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69 grid(col = \"blue\")
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70 abline(h = log2(norm_factors[j[2]]), col = \"red\", lwd = 4)
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71 maPlot(DGE\$counts[, j[1]]/DGE\$samples\$lib.size[j[1]], DGE\$counts[, j[2]]/DGE\$samples\$lib.size[j[2]], normalize = FALSE, pch = 19, cex = 0.2, ylim = c(-8, 8), main=paste(\"MA Plot\", colnames(toc)[j[1]], \"vs\", colnames(toc)[j[2]], \"Normalised\"))
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72 grid(col = \"blue\")
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73 }
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74 dev.off()
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75 pdf(file=\"$OPTIONS{e}/MDSplot.pdf\")
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76 plotMDS(DGE, main=\"MDS Plot\", col=as.numeric(factor(names(groups)))+1, xlim=c(-3,3))
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77 dev.off()
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78 tested <- list()
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79 ";
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80
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81 my $all_cont;
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82 my @add_cont;
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83 my @fact;
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84 my @fact_names;
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85 my @cp;
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86 my @cp_names;
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87 if(@ARGV) {
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88 foreach my $input (@ARGV) {
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89 my @tmp = split "::", $input;
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90 if($tmp[0] eq "factor") {
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91 $tmp[1] =~ s/[ \?\(\)\[\]\/\\=+<>:;\"\',\*\^\|\&-]/./g;
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92 push @fact_names, $tmp[1];
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93 $tmp[2] =~ s/:/\", \"/g;
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94 $tmp[2] = "\"".$tmp[2]."\"";
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95 push @fact, $tmp[2];
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96 } elsif($tmp[0] eq "cp") {
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97 $tmp[1] =~ s/[ \?\(\)\[\]\/\\=+<>:;\"\',\*\^\|\&-]/./g;
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98 push @cp_names, $tmp[1];
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99 $tmp[2] =~ s/:/, /g;
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100 push @cp, $tmp[2];
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101 } elsif($tmp[0] eq "cnt") {
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102 push @add_cont, $tmp[1];
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103 } else {
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104 die("Unknown Input: $input\n");
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105 }
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106 }
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107 }
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108
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109 if($OPTIONS{a} eq "pw") {
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110 print Rcmd "
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111 disp <- estimateCommonDisp(DGE, rowsum.filter=$OPTIONS{r})
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112 ";
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113 if(defined $OPTIONS{t}) {
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114 print Rcmd "
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115 disp <- estimateTrendedDisp (disp)
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116 disp <- estimateTagwiseDisp(disp, trend=\"$OPTIONS{u}\")
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117 pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\")
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118 plotBCV(disp, cex=0.4)
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119 abline(h=disp\$common.dispersion, col=\"firebrick\", lwd=3)
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120 dev.off()
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121 ";
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122 }
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123 print Rcmd "
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124 for(i in 1:length(pw_tests)) {
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125 tested[[i]] <- exactTest(disp, pair=pw_tests[[i]])
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126 names(tested)[i] <- paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\")
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127 }
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128 pdf(file=\"$OPTIONS{e}/Smear_Plots.pdf\")
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129 for(i in 1:length(pw_tests)) {
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130 dt <- decideTestsDGE(tested[[i]], p.value=0.05, adjust.method=\"$OPTIONS{f}\")
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131 if(sum(dt) > 0) {
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132 de_tags <- rownames(disp)[which(dt != 0)]
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133 ttl <- \"Diff. Exp. Genes With adj. Pvalue < 0.05\"
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134 } else {
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135 de_tags <- rownames(topTags(tested[[i]], n=100)\$table)
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136 ttl <- \"Top 100 tags\"
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137 }
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138
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139 if(length(dt) < 5000) {
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140 pointcex = 0.5
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141 } else {
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142 pointcex = 0.2
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143 }
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144 plotSmear(disp, pair=pw_tests[[i]], de.tags = de_tags, main = paste(\"Smear Plot\", names(tested)[i]), cex=0.5)
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145 abline(h = c(-1, 1), col = \"blue\")
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146 legend(\"topright\", c(\"2 Fold Change\", ttl) , lty=c(1, NA), pch=c(NA, 19), pt.cex=0.5, col=c(\"blue\", \"red\"), bty=\"n\")
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147 }
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148 dev.off()
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149 ";
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150 }
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151 elsif($OPTIONS{a} eq "glm") {
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152 for(my $fct = 0; $fct <= $#fact_names; $fct++) {
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153 print Rcmd "
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154 $fact_names[$fct] <- c($fact[$fct])
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155 ";
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156 }
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157 for(my $fct = 0; $fct <= $#cp_names; $fct++) {
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158 print Rcmd "
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159 $cp_names[$fct] <- c($cp[$fct])
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160 ";
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161 }
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162 my $all_fact = "";
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163 if(@fact_names) {
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164 foreach (@fact_names) {
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165 $all_fact .= " + factor($_)";
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166 }
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167 }
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168 my $all_cp = "";
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169 if(@cp_names) {
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170 $all_cp = " + ".join(" + ", @cp_names);
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171 }
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172 print Rcmd "
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173 group_fact <- factor(names(groups))
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174 design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp})
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175 colnames(design) <- sub(\"group_fact\", \"\", colnames(design))
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176 ";
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177 foreach my $fct (@fact_names) {
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178 print Rcmd "
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179 colnames(design) <- make.names(sub(\"factor.$fct.\", \"\", colnames(design)))
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180 ";
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181 }
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182 if($OPTIONS{d} eq "tag") {
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183 print Rcmd "
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184 disp <- estimateGLMCommonDisp(DGE, design)
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185 disp <- estimateGLMTrendedDisp(disp, design)
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186 disp <- estimateGLMTagwiseDisp(disp, design)
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187 fit <- glmFit(disp, design)
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188 pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\")
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189 plotBCV(disp, cex=0.4)
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190 dev.off()
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191 ";
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192 }
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193 if(@add_cont) {
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194 $all_cont = "\"".join("\", \"", @add_cont)."\"";
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195 print Rcmd "
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196 cont <- c(${all_cont})
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197 for(i in uniq_groups) cont <- gsub(paste(groups[[i]], \"([^0-9])\", sep=\"\"), paste(i, \"\\\\1\", sep=\"\"), cont)
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198 for(i in uniq_groups) cont <- gsub(paste(groups[[i]], \"\$\", sep=\"\"), i, cont)
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199 ";
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200 } else {
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201 print Rcmd "
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202 cont <- NULL
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203 ";
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204 }
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205 if(defined $OPTIONS{m}) {
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206 print Rcmd "
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207 for(i in 1:length(pw_tests)) cont <- c(cont, paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\"))
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208 ";
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209 }
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210 if(!defined $OPTIONS{m} && !@add_cont){
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211 die("No Contrasts have been specified, you must at least either select multiple pairwise comparisons or specify a custom contrast\n");
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212 }
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213 print Rcmd "
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214 fit <- glmFit(disp, design)
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215 cont <- makeContrasts(contrasts=cont, levels=design)
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216 for(i in colnames(cont)) tested[[i]] <- glmLRT(fit, contrast=cont[,i])
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217 pdf(file=\"$OPTIONS{e}/Smear_Plots.pdf\")
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218 for(i in colnames(cont)) {
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219 dt <- decideTestsDGE(tested[[i]], p.value=0.05, adjust.method=\"$OPTIONS{f}\")
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220 if(sum(dt) > 0) {
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221 de_tags <- rownames(disp)[which(dt != 0)]
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222 ttl <- \"Diff. Exp. Genes With adj. Pvalue < 0.05\"
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223 } else {
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224 de_tags <- rownames(topTags(tested[[i]], n=100)\$table)
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225 ttl <- \"Top 100 tags\"
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226 }
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227
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228 if(length(dt) < 5000) {
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229 pointcex = 0.5
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230 } else {
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231 pointcex = 0.2
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232 }
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233 plotSmear(disp, de.tags = de_tags, main = paste(\"Smear Plot\", i), cex=pointcex)
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234 abline(h = c(-1, 1), col = \"blue\")
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235 legend(\"topright\", c(\"2 Fold Change\", ttl) , lty=c(1, NA), pch=c(NA, 19), pt.cex=0.5, col=c(\"blue\", \"red\"), bty=\"n\")
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236 }
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237 dev.off()
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238
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239 ";
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240 if(defined $OPTIONS{n}) {
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241 print Rcmd "
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242 tab <- data.frame(ID=rownames(fit\$fitted.values), fit\$fitted.values, stringsAsFactors=F)
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243 write.table(tab, \"$OPTIONS{n}\", quote=F, sep=\"\\t\", row.names=F)
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244 ";
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245 }
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246 } elsif($OPTIONS{a} eq "limma") {
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247 for(my $fct = 0; $fct <= $#fact_names; $fct++) {
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248 print Rcmd "
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249 $fact_names[$fct] <- c($fact[$fct])
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250 ";
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251 }
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252 for(my $fct = 0; $fct <= $#cp_names; $fct++) {
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253 print Rcmd "
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254 $cp_names[$fct] <- c($cp[$fct])
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255 ";
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256 }
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257 my $all_fact = "";
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258 if(@fact_names) {
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259 foreach (@fact_names) {
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260 $all_fact .= " + factor($_)";
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261 }
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262 }
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263 my $all_cp = "";
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264 if(@cp_names) {
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265 $all_cp = " + ".join(" + ", @cp_names);
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266 }
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267 print Rcmd "
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268
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269 group_fact <- factor(names(groups))
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270 design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp})
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271 colnames(design) <- sub(\"group_fact\", \"\", colnames(design))
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272 ";
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273 foreach my $fct (@fact_names) {
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274 print Rcmd "
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275 colnames(design) <- make.names(sub(\"factor.$fct.\", \"\", colnames(design)))
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276 ";
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277 }
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278 print Rcmd "
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279 isexpr <- rowSums(cpm(toc)>1) >= 1
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280 toc <- toc[isexpr, ]
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281 pdf(file=\"$OPTIONS{e}/LIMMA_voom.pdf\")
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282 y <- voom(toc, design, plot=TRUE, lib.size=colSums(toc)*norm_factors)
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283 dev.off()
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284
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285 pdf(file=\"$OPTIONS{e}/LIMMA_MDS_plot.pdf\")
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286 plotMDS(y, labels=colnames(toc), col=as.numeric(factor(names(groups)))+1, gene.selection=\"common\")
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287 dev.off()
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288 fit <- lmFit(y, design)
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289 ";
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290 if(defined $OPTIONS{n}) {
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291 if(defined $OPTIONS{l}) {
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292 print Rcmd "
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293 tab <- data.frame(ID=rownames(y\$E), y\$E, stringsAsFactors=F)
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294 ";
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295 } else {
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296 print Rcmd "
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297 tab <- data.frame(ID=rownames(y\$E), 2^y\$E, stringsAsFactors=F)
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298 ";
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299 }
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300 print Rcmd "
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301 write.table(tab, \"$OPTIONS{n}\", quote=F, sep=\"\\t\", row.names=F)
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302 ";
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303 }
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304 if(@add_cont) {
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305 $all_cont = "\"".join("\", \"", @add_cont)."\"";
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306 print Rcmd "
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307 cont <- c(${all_cont})
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308 for(i in uniq_groups) cont <- gsub(paste(groups[[i]], \"([^0-9])\", sep=\"\"), paste(i, \"\\\\1\", sep=\"\"), cont)
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309 for(i in uniq_groups) cont <- gsub(paste(groups[[i]], \"\$\", sep=\"\"), i, cont)
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310 ";
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311 } else {
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312 print Rcmd "
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313 cont <- NULL
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314 ";
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315 }
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316 if(defined $OPTIONS{m}) {
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317 print Rcmd "
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318 for(i in 1:length(pw_tests)) cont <- c(cont, paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\"))
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319 ";
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320 }
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321 if(!defined $OPTIONS{m} && !@add_cont){
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322 die("No Contrasts have been specified, you must at least either select multiple pairwise comparisons or specify a custom contrast\n");
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323 }
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324 print Rcmd "
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325 cont <- makeContrasts(contrasts=cont, levels=design)
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326 fit2 <- contrasts.fit(fit, cont)
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327 fit2 <- eBayes(fit2)
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328 ";
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329 } else {
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330 die("Anaysis type $OPTIONS{a} not found\n");
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331
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332 }
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333 if($OPTIONS{a} ne "limma") {
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334 print Rcmd "
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335 options(digits = 6)
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336 tab <- NULL
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337 for(i in names(tested)) {
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338 tab_tmp <- topTags(tested[[i]], n=Inf, adjust.method=\"$OPTIONS{f}\")[[1]]
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339 colnames(tab_tmp) <- paste(i, colnames(tab_tmp), sep=\":\")
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340 tab_tmp <- tab_tmp[tagnames,]
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341 if(is.null(tab)) {
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342 tab <- tab_tmp
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343 } else tab <- cbind(tab, tab_tmp)
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344 }
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345 tab <- cbind(Feature=rownames(tab), tab)
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346 ";
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347 } else {
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348 print Rcmd "
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349 tab <- NULL
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350 options(digits = 6)
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351 for(i in colnames(fit2)) {
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352 tab_tmp <- topTable(fit2, coef=i, n=Inf, sort.by=\"none\", adjust.method=\"$OPTIONS{f}\")
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353 colnames(tab_tmp)[-1] <- paste(i, colnames(tab_tmp)[-1], sep=\":\")
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354 if(is.null(tab)) {
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355 tab <- tab_tmp
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356 } else tab <- cbind(tab, tab_tmp)
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357 }
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358 tab <- cbind(Feature=rownames(tab), tab)
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359 ";
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360 }
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361 print Rcmd "
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362 write.table(tab, \"$OPTIONS{o}\", quote=F, sep=\"\\t\", row.names=F)
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363 sink(type=\"message\")
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364 sink()
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365 ";
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366 close(Rcmd);
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367 system("R --no-restore --no-save --no-readline < $OPTIONS{e}/r_script.R > $OPTIONS{e}/r_script.out");
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368
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369 open(HTML, ">$OPTIONS{h}");
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370 print HTML "<html><head><title>EdgeR: Empirical analysis of digital gene expression data</title></head><body><h3>EdgeR Additional Files:</h3><p><ul>\n";
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371 print HTML "<li><a href=MA_plots_normalisation.pdf>MA_plots_normalisation.pdf</a></li>\n";
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372 print HTML "<li><a href=MDSplot.pdf>MDSplot.pdf</a></li>\n";
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373 if($OPTIONS{a} eq "pw") {
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374 if(defined $OPTIONS{t}) {
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375 print HTML "<li><a href=Tagwise_Dispersion_vs_Abundance.pdf>Tagwise_Dispersion_vs_Abundance.pdf</a></li>\n";
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376 }
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377 print HTML "<li><a href=Smear_Plots.pdf>Smear_Plots.pdf</a></li>\n";
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378 } elsif($OPTIONS{a} eq "glm" && $OPTIONS{d} eq "tag") {
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379 print HTML "<li><a href=Tagwise_Dispersion_vs_Abundance.pdf>Tagwise_Dispersion_vs_Abundance.pdf</a></li>\n";
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380 print HTML "<li><a href=Smear_Plots.pdf>Smear_Plots.pdf</a></li>\n";
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|
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381 } elsif($OPTIONS{a} eq "limma") {
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382 print HTML "<li><a href=LIMMA_MDS_plot.pdf>LIMMA_MDS_plot.pdf</a></li>\n";
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383 print HTML "<li><a href=LIMMA_voom.pdf>LIMMA_voom.pdf</a></li>\n";
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384 }
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385 print HTML "<li><a href=r_script.R>r_script.R</a></li>\n";
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386 print HTML "<li><a href=r_script.out>r_script.out</a></li>\n";
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387 print HTML "<li><a href=r_script.err>r_script.err</a></li>\n";
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388 print HTML "</ul></p>\n";
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389 close(HTML);
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390
|
