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annotate edgeR_repenrich.R @ 12:89e05f831259 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/repenrich commit 212b838f614f1f7b8e770473c026d9c1180722df
author artbio
date Mon, 18 Mar 2024 09:39:44 +0000
parents 6bba3e33c2e7
children 530626b0757c
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f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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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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f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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7 # To not crash galaxy with an UTF8 error with not-US LC settings.
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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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 # we read the options from the default: commandArgs(TRUE).
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23 spec <- matrix(
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24 c(
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25 "quiet", "q", 0, "logical",
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26 "outfile", "o", 1, "character",
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27 "countsfile", "n", 1, "character",
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28 "factorName", "N", 1, "character",
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29 "levelNameA", "A", 1, "character",
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30 "levelNameB", "B", 1, "character",
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31 "levelAfiles", "a", 1, "character",
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32 "levelBfiles", "b", 1, "character",
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33 "alignmentA", "i", 1, "character",
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34 "alignmentB", "j", 1, "character",
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35 "plots", "p", 1, "character"
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36 ),
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37 byrow = TRUE, ncol = 4
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38 )
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39 opt <- getopt(spec)
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41 # build levels A and B file lists
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42 filesA <- fromJSON(opt$levelAfiles, method = "C", unexpected.escape = "error")
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43 filesB <- fromJSON(opt$levelBfiles, method = "C", unexpected.escape = "error")
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44 listA <- list()
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45 indice <- 0
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46 listA[["level"]] <- opt$levelNameA
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47 for (file in filesA) {
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48 indice <- indice + 1
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49 listA[[paste0(opt$levelNameA, "_", indice)]] <- read.delim(file, header = FALSE)
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50 }
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51 listB <- list()
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52 indice <- 0
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53 listB[["level"]] <- opt$levelNameB
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54 for (file in filesB) {
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55 indice <- indice + 1
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56 listB[[paste0(opt$levelNameB, "_", indice)]] <- read.delim(file, header = FALSE)
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57 }
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59 # build a counts table
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60 counts <- data.frame(row.names = listA[[2]][, 1])
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61 for (element in names(listA[-1])) {
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62 counts <- cbind(counts, listA[[element]][, 4])
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63 }
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64 for (element in names(listB[-1])) {
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65 counts <- cbind(counts, listB[[element]][, 4])
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66 }
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67 colnames(counts) <- c(names(listA[-1]), names(listB[-1]))
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69 # build aligned counts vector
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70 filesi <- fromJSON(opt$alignmentA, method = "C", unexpected.escape = "error")
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71 filesj <- fromJSON(opt$alignmentB, method = "C", unexpected.escape = "error")
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72 sizes <- c()
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73 for (file in filesi) {
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74 sizes <- c(sizes, read.delim(file, header = TRUE)[1, 1])
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75 }
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76 for (file in filesj) {
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77 sizes <- c(sizes, read.delim(file, header = TRUE)[1, 1])
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78 }
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80 # build a meta data object
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81 meta <- data.frame(
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82 row.names = colnames(counts),
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83 condition = c(rep(opt$levelNameA, length(filesA)), rep(opt$levelNameB, length(filesB))),
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84 libsize = sizes
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85 )
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86
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87
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88 # Define the library size and conditions for the GLM
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89 libsize <- meta$libsize
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90 condition <- factor(meta$condition)
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91 design <- model.matrix(~ 0 + condition)
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92 colnames(design) <- levels(condition)
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93
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94 # Build a DGE object for the GLM
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95 y <- DGEList(counts = counts, lib.size = libsize)
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96
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97 # Normalize the data
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98 y <- calcNormFactors(y)
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99
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100 # Estimate the variance
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101 y <- estimateGLMCommonDisp(y, design)
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102 y <- estimateGLMTrendedDisp(y, design)
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103 y <- estimateGLMTagwiseDisp(y, design)
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104
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105 # Builds and outputs an object to contain the normalized read abundance in counts per million of reads
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106 cpm <- cpm(y, log = FALSE, lib.size = libsize)
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107 cpm <- as.data.frame(cpm)
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108 colnames(cpm) <- colnames(counts)
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109 if (!is.null(opt$countsfile)) {
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110 normalizedAbundance <- data.frame(Tag = rownames(cpm))
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111 normalizedAbundance <- cbind(normalizedAbundance, cpm)
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112 write.table(normalizedAbundance, file = opt$countsfile, sep = "\t", col.names = TRUE, row.names = FALSE, quote = FALSE)
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113 }
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114
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115 # Conduct fitting of the GLM
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116 yfit <- glmFit(y, design)
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117
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118 # Initialize result matrices to contain the results of the GLM
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119 results <- matrix(nrow = dim(counts)[1], ncol = 0)
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120 logfc <- matrix(nrow = dim(counts)[1], ncol = 0)
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121
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122 # Make the comparisons for the GLM
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123 my.contrasts <- makeContrasts(
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124 paste0(opt$levelNameA, "_", opt$levelNameB, " = ", opt$levelNameA, " - ", opt$levelNameB),
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125 levels = design
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126 )
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127
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128 # Define the contrasts used in the comparisons
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129 allcontrasts <- paste0(opt$levelNameA, " vs ", opt$levelNameB)
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130
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131 # Conduct a for loop that will do the fitting of the GLM for each comparison
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132 # Put the results into the results objects
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133 lrt <- glmLRT(yfit, contrast = my.contrasts[, 1])
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134 res <- topTags(lrt, n = dim(c)[1], sort.by = "none")$table
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135 results <- cbind(results, res[, c(1, 5)])
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136 logfc <- cbind(logfc, res[c(1)])
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137
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138 # Add the repeat types back into the results.
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139 # We should still have the same order as the input data
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140 results$class <- listA[[2]][, 2]
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141 results$type <- listA[[2]][, 3]
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142 # Sort the results table by the FDR
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143 results <- results[with(results, order(FDR)), ]
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144
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145 # Plot Fold Changes for repeat classes and types
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146
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147 # open the device and plots
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148 if (!is.null(opt$plots)) {
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149 pdf(opt$plots)
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150 plotMDS(y, main = "Multidimensional Scaling Plot Of Distances Between Samples")
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151 plotBCV(y, xlab = "Gene abundance (Average log CPM)", main = "Biological Coefficient of Variation Plot")
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152 logFC <- results[, "logFC"]
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153 # Plot the repeat classes
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154 classes <- with(results, reorder(class, -logFC, median))
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155 classes
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156 par(mar = c(6, 10, 4, 1))
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157 boxplot(logFC ~ classes,
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158 data = results, outline = FALSE, horizontal = TRUE,
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159 las = 2, xlab = "log2(Fold Change)", ylab = "", cex.axis = 0.7, main = paste0(allcontrasts, ", by Class")
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160 )
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161 abline(v = 0)
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162 # Plot the repeat types
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163 types <- with(results, reorder(type, -logFC, median))
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164 boxplot(logFC ~ types,
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165 data = results, outline = FALSE, horizontal = TRUE,
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166 las = 2, xlab = "log2(Fold Change)", ylab = "", cex.axis = 0.7, main = paste0(allcontrasts, ", by Type")
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167 )
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168 abline(v = 0)
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169 # volcano plot
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170 TEdata <- cbind(rownames(results), as.data.frame(results), score = -log(results$FDR, 10))
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171 colnames(TEdata) <- c("Tag", "log2FC", "FDR", "Class", "Type", "score")
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172 color <- ifelse(TEdata$FDR < 0.05, "red", "black")
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173 s <- subset(TEdata, FDR < 0.01)
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174 with(TEdata, plot(log2FC, score, pch = 20, col = color, main = "Volcano plot (all tag types)", ylab = "-log10(FDR)"))
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175 text(s[, 2], s[, 6], labels = s[, 5], pos = seq(from = 1, to = 3), cex = 0.5)
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176 }
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177
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178 # close the plot device
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179 if (!is.null(opt$plots)) {
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180 cat("closing plot device\n")
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181 dev.off()
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182 }
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183
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184 # Save the results
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185 results <- cbind(TE_item = rownames(results), results)
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186 colnames(results) <- c("TE_item", "log2FC", "FDR", "Class", "Type")
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187 results$log2FC <- format(results$log2FC, digits = 5)
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188 results$FDR <- format(results$FDR, digits = 5)
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189 write.table(results, opt$outfile, quote = FALSE, sep = "\t", col.names = TRUE, row.names = FALSE)
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190
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191 cat("Session information:\n\n")
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192 sessionInfo()