annotate edgeR_repenrich.R @ 15:2e3d976e7d5d draft default tip

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 03183e29f807ec33548016a7c4144f52720b7b9e
author artbio
date Sun, 21 Apr 2024 09:44:51 +0000
parents 530626b0757c
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1 # setup R error handling to go to stderr
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2 options(show.error.messages = FALSE, error = function() {
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3 cat(geterrmessage(), file = stderr())
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4 q("no", 1, FALSE)
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5 })
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7 # To not crash galaxy with an UTF8 error with not-US LC settings.
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8 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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9
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10 # load libraries
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11 library("getopt")
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12 library("tools")
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13 library("rjson")
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14 suppressPackageStartupMessages({
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15 library("edgeR")
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16 library("limma")
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17 })
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19 options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
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21 # get options, using the spec as defined by the enclosed list.
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22 spec <- matrix(
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23 c(
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24 "quiet", "q", 0, "logical",
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25 "outfile", "o", 1, "character",
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26 "countsfile", "n", 1, "character",
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27 "factorName", "N", 1, "character",
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28 "levelNameA", "A", 1, "character",
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29 "levelNameB", "B", 1, "character",
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30 "levelAfiles", "a", 1, "character",
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31 "levelBfiles", "b", 1, "character",
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32 "plots", "p", 1, "character"
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33 ),
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34 byrow = TRUE, ncol = 4
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35 )
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36 opt <- getopt(spec)
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38 # build levels A and B file lists
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39 filesA <- fromJSON(opt$levelAfiles, method = "C", unexpected.escape = "error")
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40 filesB <- fromJSON(opt$levelBfiles, method = "C", unexpected.escape = "error")
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41 listA <- list()
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42 indice <- 0
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43 listA[["level"]] <- opt$levelNameA
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44 for (file in filesA) {
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45 indice <- indice + 1
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46 listA[[paste0(opt$levelNameA, "_", indice)]] <- read.delim(file, header = FALSE)
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47 }
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48 listB <- list()
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49 indice <- 0
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50 listB[["level"]] <- opt$levelNameB
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51 for (file in filesB) {
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52 indice <- indice + 1
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53 listB[[paste0(opt$levelNameB, "_", indice)]] <- read.delim(file, header = FALSE)
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54 }
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56 # build a counts table
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57 counts <- data.frame(row.names = listA[[2]][, 1])
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58 for (element in names(listA[-1])) {
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59 counts <- cbind(counts, listA[[element]][, 4])
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60 }
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61 for (element in names(listB[-1])) {
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62 counts <- cbind(counts, listB[[element]][, 4])
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63 }
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64 colnames(counts) <- c(names(listA[-1]), names(listB[-1]))
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65 sizes <- colSums(counts)
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67 # build a meta data object
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68 meta <- data.frame(
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69 row.names = colnames(counts),
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70 condition = c(rep(opt$levelNameA, length(filesA)), rep(opt$levelNameB, length(filesB))),
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71 libsize = sizes
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72 )
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75 # Define the library size and conditions for the GLM
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76 libsize <- meta$libsize
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77 condition <- factor(meta$condition)
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78 design <- model.matrix(~ 0 + condition)
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79 colnames(design) <- levels(condition)
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81 # Build a DGE object for the GLM
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82 y <- DGEList(counts = counts, lib.size = libsize)
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84 # Normalize the data
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85 y <- calcNormFactors(y)
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86
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87 # Estimate the variance
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88 y <- estimateGLMCommonDisp(y, design)
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89 y <- estimateGLMTrendedDisp(y, design)
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90 y <- estimateGLMTagwiseDisp(y, design)
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91
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92 # Builds and outputs an object to contain the normalized read abundance in counts per million of reads
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93 cpm <- cpm(y, log = FALSE, lib.size = libsize)
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94 cpm <- as.data.frame(cpm)
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95 colnames(cpm) <- colnames(counts)
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96 if (!is.null(opt$countsfile)) {
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97 normalizedAbundance <- data.frame(Tag = rownames(cpm))
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98 normalizedAbundance <- cbind(normalizedAbundance, cpm)
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99 write.table(normalizedAbundance, file = opt$countsfile, sep = "\t", col.names = TRUE, row.names = FALSE, quote = FALSE)
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100 }
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101
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102 # Conduct fitting of the GLM
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103 yfit <- glmFit(y, design)
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104
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105 # Initialize result matrices to contain the results of the GLM
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106 results <- matrix(nrow = dim(counts)[1], ncol = 0)
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107 logfc <- matrix(nrow = dim(counts)[1], ncol = 0)
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108
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109 # Make the comparisons for the GLM
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110 my.contrasts <- makeContrasts(
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111 paste0(opt$levelNameA, "_", opt$levelNameB, " = ", opt$levelNameA, " - ", opt$levelNameB),
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112 levels = design
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113 )
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114
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115 # Define the contrasts used in the comparisons
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116 allcontrasts <- paste0(opt$levelNameA, " vs ", opt$levelNameB)
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117
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118 # Conduct a for loop that will do the fitting of the GLM for each comparison
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119 # Put the results into the results objects
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120 lrt <- glmLRT(yfit, contrast = my.contrasts[, 1])
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121 res <- topTags(lrt, n = dim(c)[1], sort.by = "none")$table
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122 results <- cbind(results, res[, c(1, 5)])
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123 logfc <- cbind(logfc, res[c(1)])
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124
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125 # Add the repeat types back into the results.
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126 # We should still have the same order as the input data
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127 results$class <- listA[[2]][, 2]
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128 results$type <- listA[[2]][, 3]
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129 # Sort the results table by the FDR
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130 results <- results[with(results, order(FDR)), ]
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131
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132 # Plot Fold Changes for repeat classes and types
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133
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134 # open the device and plots
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135 if (!is.null(opt$plots)) {
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136 pdf(opt$plots)
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137 plotMDS(y, main = "Multidimensional Scaling Plot Of Distances Between Samples")
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138 plotBCV(y, xlab = "Gene abundance (Average log CPM)", main = "Biological Coefficient of Variation Plot")
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139 logFC <- results[, "logFC"]
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140 # Plot the repeat classes
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141 classes <- with(results, reorder(class, -logFC, median))
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142 classes
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143 par(mar = c(6, 10, 4, 1))
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144 boxplot(logFC ~ classes,
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145 data = results, outline = FALSE, horizontal = TRUE,
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146 las = 2, xlab = "log2(Fold Change)", ylab = "", cex.axis = 0.7, main = paste0(allcontrasts, ", by Class")
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147 )
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148 abline(v = 0)
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149 # Plot the repeat types
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150 types <- with(results, reorder(type, -logFC, median))
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151 boxplot(logFC ~ types,
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152 data = results, outline = FALSE, horizontal = TRUE,
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153 las = 2, xlab = "log2(Fold Change)", ylab = "", cex.axis = 0.7, main = paste0(allcontrasts, ", by Type")
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154 )
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155 abline(v = 0)
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156 # volcano plot
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157 TEdata <- cbind(rownames(results), as.data.frame(results), score = -log(results$FDR, 10))
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158 colnames(TEdata) <- c("Tag", "log2FC", "FDR", "Class", "Type", "score")
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159 color <- ifelse(TEdata$FDR < 0.05, "red", "black")
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160 s <- subset(TEdata, FDR < 0.01)
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161 with(TEdata, plot(log2FC, score, pch = 20, col = color, main = "Volcano plot (all tag types)", ylab = "-log10(FDR)"))
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162 text(s[, 2], s[, 6], labels = s[, 5], pos = seq(from = 1, to = 3), cex = 0.5)
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163 }
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164
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165 # close the plot device
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166 if (!is.null(opt$plots)) {
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167 cat("closing plot device\n")
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168 dev.off()
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169 }
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170
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171 # Save the results
6bba3e33c2e7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 11df9f0b68d35d3a9424a17e4cefee6cfb9d4c19
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parents: 8
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172 results <- cbind(TE_item = rownames(results), results)
6bba3e33c2e7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 11df9f0b68d35d3a9424a17e4cefee6cfb9d4c19
artbio
parents: 8
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173 colnames(results) <- c("TE_item", "log2FC", "FDR", "Class", "Type")
6bba3e33c2e7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 11df9f0b68d35d3a9424a17e4cefee6cfb9d4c19
artbio
parents: 8
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174 results$log2FC <- format(results$log2FC, digits = 5)
6bba3e33c2e7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 11df9f0b68d35d3a9424a17e4cefee6cfb9d4c19
artbio
parents: 8
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175 results$FDR <- format(results$FDR, digits = 5)
6bba3e33c2e7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 11df9f0b68d35d3a9424a17e4cefee6cfb9d4c19
artbio
parents: 8
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176 write.table(results, opt$outfile, quote = FALSE, sep = "\t", col.names = TRUE, row.names = FALSE)