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annotate edgeR_repenrich.R @ 6:83b5ea7cc342 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit e3c9f70fe670ee27d667cabd85bf820f63c94f7a
author artbio
date Fri, 07 Dec 2018 13:05:46 -0500
parents 15e3e29f310e
children 308591fd36e5
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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 #!/usr/bin/env Rscript
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3 # A command-line interface to edgeR for use with Galaxy edger-repenrich
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4 # written by Christophe Antoniewski drosofff@gmail.com 2017.05.30
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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 # setup R error handling to go to stderr
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8 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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10 # To not crash galaxy with an UTF8 error with not-US LC settings.
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11 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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13 library("getopt")
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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14 library("tools")
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15 options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
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16 args <- commandArgs(trailingOnly = TRUE)
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18 # get options, using the spec as defined by the enclosed list.
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19 # we read the options from the default: commandArgs(TRUE).
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20 spec <- matrix(c(
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21 "quiet", "q", 0, "logical",
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22 "help", "h", 0, "logical",
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23 "outfile", "o", 1, "character",
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24 "countsfile", "n", 1, "character",
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25 "factorName", "N", 1, "character",
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26 "levelNameA", "A", 1, "character",
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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27 "levelNameB", "B", 1, "character",
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28 "levelAfiles", "a", 1, "character",
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29 "levelBfiles", "b", 1, "character",
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30 "alignmentA", "i", 1, "character",
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31 "alignmentB", "j", 1, "character",
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32 "plots" , "p", 1, "character"),
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33 byrow=TRUE, ncol=4)
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34 opt <- getopt(spec)
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36 # if help was asked for print a friendly message
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37 # and exit with a non-zero error code
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38 if (!is.null(opt$help)) {
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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39 cat(getopt(spec, usage=TRUE))
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40 q(status=1)
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41 }
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43 # enforce the following required arguments
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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44 if (is.null(opt$outfile)) {
f6f0f1e5e940 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 61e203df0be5ed877ff92b917c7cde6eeeab8310
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45 cat("'outfile' is required\n")
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46 q(status=1)
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47 }
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48 if (is.null(opt$levelAfiles) | is.null(opt$levelBfiles)) {
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49 cat("input count files are required for both levels\n")
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50 q(status=1)
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51 }
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52 if (is.null(opt$alignmentA) | is.null(opt$alignmentB)) {
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53 cat("total aligned read files are required for both levels\n")
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54 q(status=1)
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55 }
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56
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57 verbose <- if (is.null(opt$quiet)) {
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58 TRUE
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59 } else {
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60 FALSE
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61 }
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62
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63 suppressPackageStartupMessages({
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64 library("edgeR")
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65 library("limma")
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66 })
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68 # build levels A and B file lists
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69
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70 library("rjson")
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71 filesA <- fromJSON(opt$levelAfiles, method = "C", unexpected.escape = "error")
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72 filesB <- fromJSON(opt$levelBfiles, method = "C", unexpected.escape = "error")
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73 listA <- list()
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74 indice = 0
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75 listA[["level"]] <- opt$levelNameA
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76 for (file in filesA) {
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77 indice = indice +1
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78 listA[[paste0(opt$levelNameA,"_",indice)]] <- read.delim(file, header=FALSE)
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79 }
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80 listB <- list()
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81 indice = 0
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82 listB[["level"]] <- opt$levelNameB
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83 for (file in filesB) {
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84 indice = indice +1
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85 listB[[paste0(opt$levelNameB,"_",indice)]] <- read.delim(file, header=FALSE)
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86 }
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87
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88 # build a counts table
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89 counts <- data.frame(row.names=listA[[2]][,1])
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90 for (element in names(listA[-1])) {
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91 counts<-cbind(counts, listA[[element]][,4])
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92 }
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93 for (element in names(listB[-1])) {
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94 counts<-cbind(counts, listB[[element]][,4])
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95 }
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96 colnames(counts)=c(names(listA[-1]), names(listB[-1]))
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97
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98 # build aligned counts vector
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99
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100 filesi <- fromJSON(opt$alignmentA, method = "C", unexpected.escape = "error")
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101 filesj <- fromJSON(opt$alignmentB, method = "C", unexpected.escape = "error")
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102 sizes <- c()
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103 for (file in filesi) {
2
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104 sizes <- c(sizes, read.delim(file, header=TRUE)[1,1])
0
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105 }
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106 for (file in filesj) {
2
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107 sizes <- c(sizes, read.delim(file, header=TRUE)[1,1])
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108 }
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109
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110 # build a meta data object
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111
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112 meta <- data.frame(
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113 row.names=colnames(counts),
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114 condition=c(rep(opt$levelNameA,length(filesA)), rep(opt$levelNameB,length(filesB)) ),
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115 libsize=sizes
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116 )
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117
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118
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119 # Define the library size and conditions for the GLM
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120 libsize <- meta$libsize
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121 condition <- factor(meta$condition)
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122 design <- model.matrix(~0+condition)
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123 colnames(design) <- levels(meta$condition)
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124
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125
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126 # Build a DGE object for the GLM
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127 y <- DGEList(counts=counts, lib.size=libsize)
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128
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129 # Normalize the data
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130 y <- calcNormFactors(y)
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131 y$samples
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132 # plotMDS(y) latter
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133
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134 # Estimate the variance
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135 y <- estimateGLMCommonDisp(y, design)
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136 y <- estimateGLMTrendedDisp(y, design)
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137 y <- estimateGLMTagwiseDisp(y, design)
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138 # plotBCV(y) latter
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139
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140 # Builds and outputs an object to contain the normalized read abundance in counts per million of reads
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141 cpm <- cpm(y, log=FALSE, lib.size=libsize)
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142 cpm <- as.data.frame(cpm)
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143 colnames(cpm) <- colnames(counts)
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144 if (!is.null(opt$countsfile)) {
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145 normalizedAbundance <- data.frame(Tag=rownames(cpm))
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146 normalizedAbundance <- cbind(normalizedAbundance, cpm)
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147 write.table(normalizedAbundance, file=opt$countsfile, sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)
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148 }
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149
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150 # Conduct fitting of the GLM
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151 yfit <- glmFit(y, design)
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152
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153 # Initialize result matrices to contain the results of the GLM
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154 results <- matrix(nrow=dim(counts)[1],ncol=0)
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155 logfc <- matrix(nrow=dim(counts)[1],ncol=0)
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156
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157 # Make the comparisons for the GLM
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158 my.contrasts <- makeContrasts(
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159 paste0(opt$levelNameA,"_",opt$levelNameB," = ", opt$levelNameA, " - ", opt$levelNameB),
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160 levels = design
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161 )
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162
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163 # Define the contrasts used in the comparisons
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164 allcontrasts = paste0(opt$levelNameA," vs ",opt$levelNameB)
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165
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166 # Conduct a for loop that will do the fitting of the GLM for each comparison
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167 # Put the results into the results objects
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168 lrt <- glmLRT(yfit, contrast=my.contrasts[,1])
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169 plotSmear(lrt, de.tags=rownames(y))
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170 title(allcontrasts)
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171 res <- topTags(lrt,n=dim(c)[1],sort.by="none")$table
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172 results <- cbind(results,res[,c(1,5)])
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173 logfc <- cbind(logfc,res[c(1)])
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174
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175 # Add the repeat types back into the results.
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176 # We should still have the same order as the input data
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177 results$class <- listA[[2]][,2]
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178 results$type <- listA[[2]][,3]
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179
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180 # Sort the results table by the FDR
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181 results <- results[with(results, order(FDR)), ]
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182
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183 # Save the results
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184 write.table(results, opt$outfile, quote=FALSE, sep="\t", col.names=FALSE)
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185
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186 # Plot Fold Changes for repeat classes and types
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187
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188 # open the device and plots
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189 if (!is.null(opt$plots)) {
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190 if (verbose) cat("creating plots\n")
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191 pdf(opt$plots)
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192 plotMDS(y, main="Multidimensional Scaling Plot Of Distances Between Samples")
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193 plotBCV(y, xlab="Gene abundance (Average log CPM)", main="Biological Coefficient of Variation Plot")
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194 logFC <- results[, "logFC"]
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195 # Plot the repeat classes
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196 classes <- with(results, reorder(class, -logFC, median))
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197 classes
0
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198 par(mar=c(6,10,4,1))
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199 boxplot(logFC ~ classes, data=results, outline=FALSE, horizontal=TRUE,
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200 las=2, xlab="log2(Fold Change)", main=paste0(allcontrasts, ", by Class"))
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201 abline(v=0)
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202 # Plot the repeat types
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203 types <- with(results, reorder(type, -logFC, median))
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204 boxplot(logFC ~ types, data=results, outline=FALSE, horizontal=TRUE,
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205 las=2, xlab="log2(Fold Change)", main=paste0(allcontrasts, ", by Type"), yaxt="n")
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206 axis(2, cex.axis=(1*16)/(length(levels(types))),
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207 at=seq(from=1, to=length(levels(types))),
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208 labels=levels(types), las=2)
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209 abline(v=0)
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210 # volcano plot
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211 TEdata = cbind(rownames(results), as.data.frame(results), score=-log(results$FDR, 10))
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212 colnames(TEdata) = c("Tag","log2FC", "FDR", "Class", "Type", "score")
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213 color = ifelse(TEdata$FDR<0.05, "red", "black")
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214 s <- subset(TEdata, FDR<0.01)
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215 with(TEdata, plot(log2FC, score, pch=20, col=color, main="Volcano plot (all tag types)", ylab="-log10(FDR)"))
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216 text(s[,2], s[,6], labels = s[,5], pos = seq(from=1, to=3), cex=0.5)
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217 }
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218
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219 # close the plot device
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220 if (!is.null(opt$plots)) {
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221 cat("closing plot device\n")
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222 dev.off()
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223 }
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224
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225 cat("Session information:\n\n")
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226
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227 sessionInfo()
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228