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annotate ruvseq.R @ 0:61dffb20b6f9 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/ruvseq commit b95582cea8320d5488056a9576474f79cec53be8
author iuc
date Wed, 05 Sep 2018 15:54:16 -0400
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children c24765926774
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61dffb20b6f9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/ruvseq commit b95582cea8320d5488056a9576474f79cec53be8
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1 # setup R error handling to go to stderr
61dffb20b6f9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/ruvseq commit b95582cea8320d5488056a9576474f79cec53be8
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2 library("getopt")
61dffb20b6f9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/ruvseq commit b95582cea8320d5488056a9576474f79cec53be8
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3 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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4 options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
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5
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6 setup_cmdline_options <- function() {
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7 args <- commandArgs(trailingOnly = TRUE)
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8 spec <- matrix(c(
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9 "help", "h", 0, "logical",
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10 "alpha", "a", 1, "double",
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11 "min_mean_count", "min_c", 1, "double",
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12 "min_k", "min_k", 1, "double",
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13 "max_k", "max_k", 1, "double",
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14 "sample_json", "s", 1, "character",
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15 "plots" , "p", 1, "character",
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16 "header", "H", 0, "logical",
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17 "txtype", "y", 1, "character",
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18 "tx2gene", "x", 1, "character"), # a space-sep tx-to-gene map or GTF file (auto detect .gtf/.GTF)
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19 byrow=TRUE, ncol=4)
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20
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21 opt <- getopt(spec)
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22 # if help was asked for print a friendly message
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23 # and exit with a non-zero error code
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24 if (!is.null(opt$help)) {
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25 cat(getopt(spec, usage=TRUE))
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26 q(status=1)
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27 } else {
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28 load_libraries()
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29 }
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30 return(opt)
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31 }
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32
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33 load_libraries <- function() {
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34 # Allows displaying help without waiting for libraries to load
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35 library("tools")
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36 library("jsonlite")
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37 library("reshape2")
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38 library("RUVSeq")
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39 library("RColorBrewer")
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40 library("tximport")
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41 library("DESeq2")
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42 library("ggrepel")
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43 }
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44
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45 source_local <- function(fname){
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46 argv <- commandArgs(trailingOnly = FALSE)
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47 base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
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48 source(paste(base_dir, fname, sep="/"))
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49 }
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50
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51 # Source get_deseq_dataset.R for getting deseq dataset from htseq/featurecounts/tximport
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52 source_local('get_deseq_dataset.R')
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53
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54 # RUVseq function definitions
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55
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56 plot_pca_rle <- function (set, title) {
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57 x <- pData(set)[,1]
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58 colors <- brewer.pal(3, "Set2")
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59 label <- paste0(' for ', title)
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60 plotRLE(set, outline=FALSE, ylim=c(-4, 4), col=colors[x])
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61 title(main=paste0("RLE", label))
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62 plotPCA(set, col=colors[x], cex=1.2)
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63 title(main=paste0("PCA", label))
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64 }
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65
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66 plot_factors_of_unwanted_variation <- function(set, method, k){
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67 pd <- pData(set)
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68 pd['sample'] <- row.names(pd)
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69 colnames(pd)[1] <- 'condition'
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70 d = melt(pd, id.vars = c('sample', 'condition'))
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71 d['x'] <- 1 # There is no information on the X, so we just fake it to be able to do a scatterplot
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72 print(ggplot(d, aes(x=x, y=value, color=condition, label=sample)) +
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73 geom_point() +
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74 ggtitle(paste0('Factors of unwanted variation for method: ', method, ", k=", k)) +
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75 facet_wrap( ~ variable, scales = "free_x") +
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76 geom_text_repel() +
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77 theme(axis.title.x=element_blank(),
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78 axis.text.x=element_blank(),
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79 axis.ticks.x=element_blank(),
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80 plot.title = element_text(hjust = 0.5))
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81 )
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82 }
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83
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84 create_seq_expression_set <- function (dds, min_mean_count) {
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85 count_values <- counts(dds)
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86 filter <- apply(count_values, 1, function(x) mean(x) > min_mean_count)
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87 filtered <- count_values[filter,]
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88 set = newSeqExpressionSet(as.matrix(count_values),
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89 phenoData = data.frame(colData(dds)$condition, row.names=colnames(filtered)))
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90 plot_pca_rle(set = set, title = 'raw data')
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91 set <- betweenLaneNormalization(set, which="upper")
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92 plot_pca_rle(set = set, title = 'upper quartile normalized')
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93 return(set)
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94 }
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95
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96 get_empirical_control_genes <- function(set, cutoff_p) {
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97 x <- pData(set)[,1]
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98 design <- model.matrix(~x, data=pData(set))
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99 y <- DGEList(counts=counts(set), group=x)
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100 y <- calcNormFactors(y, method="upperquartile")
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101 y <- estimateGLMCommonDisp(y, design)
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102 y <- estimateGLMTagwiseDisp(y, design)
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103 fit <- glmFit(y, design)
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104 lrt <- glmLRT(fit, coef=2)
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105 top <- topTags(lrt, n=nrow(set))$table
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106 top_rows <- rownames(top)[which(top$PValue > cutoff_p)]
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107 empirical <- rownames(set)[which(!(rownames(set) %in% top_rows))]
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108 return(empirical)
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109 }
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110
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111 ruv_control_gene_method <- function(set, k, control_genes='empirical', cutoff_p=0.2) {
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112 if (control_genes == 'empirical') {
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113 control_genes = get_empirical_control_genes(set, cutoff_p=cutoff_p)
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114 }
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115 set <- RUVg(set, control_genes, k=k)
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116 plot_pca_rle(set, paste0("RUVg with empirical control genes, k=", k))
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117 plot_factors_of_unwanted_variation(set, method="RUVg with empirical control genes", k=k)
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118 return(set)
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119 }
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120
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121 ruv_residual_method <- function(set, k) {
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122 genes <- rownames(counts(set))
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123 x <- pData(set)[,1]
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124 # Initial edger residuals
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125 design <- model.matrix(~x, data=pData(set))
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126 y <- DGEList(counts=counts(set), group=x)
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127 y <- calcNormFactors(y, method="upperquartile")
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128 y <- estimateGLMCommonDisp(y, design)
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129 y <- estimateGLMTagwiseDisp(y, design)
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130 fit <- glmFit(y, design)
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131 res <- residuals(fit, type="deviance")
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132 set <- RUVr(set, genes, k=k, res)
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133 plot_pca_rle(set = set, title = paste0('RUVr using residuals, k=', k))
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134 plot_factors_of_unwanted_variation(set, method="RUVr using residuals", k=k)
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135 return(set)
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136 }
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137
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138 ruv_replicate_method <- function (set, k) {
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139 genes <- rownames(counts(set))
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140 x <- pData(set)[,1]
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141 differences <- makeGroups(x)
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142 set <- RUVs(set, genes, k=k, differences)
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143 plot_pca_rle(set, paste0('RUVs with replicate samples, k=', k))
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144 plot_factors_of_unwanted_variation(set, method="RUVs using replicates", k=k)
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145 return(set)
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146 }
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147
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148 get_differentially_expressed_genes <- function(dds, contrast, alpha=0.01) {
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149 r <- results(dds, contrast=contrast, alpha=alpha)
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150 return(rownames(r[which(r$padj < alpha),]))
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151 }
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152
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153 opt <- setup_cmdline_options()
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154 alpha <- opt$alpha
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155 min_k <- opt$min_k
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156 max_k <- opt$max_k
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157 sample_json <- fromJSON(opt$sample_json)
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158 sample_paths <- sample_json$path
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159 sample_names <- sample_json$label
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160 condition <- as.factor(sample_json$condition)
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161 sampleTable <- data.frame(samplename=sample_names,
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162 filename = sample_paths,
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163 condition=condition)
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164 rownames(sampleTable) <- sample_names
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165
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166 dds <- get_deseq_dataset(sampleTable, header=opt$header, designFormula= ~ condition, tximport=opt$txtype, txtype=opt$txtype, tx2gene=opt$tx2gene)
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167 if (!is.null(opt$plots)) {
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168 pdf(opt$plots)
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169 }
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170
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171 # Run through the ruvseq variants
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172 set <- create_seq_expression_set(dds, min_mean_count = opt$min_mean_count)
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173 result <- list(no_correction = set)
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174 for (k in seq(min_k, max_k)) {
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175 result[[paste0('residual_method_k', k)]] <- ruv_residual_method(set, k=k)
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176 result[[paste0('replicate_method_k', k)]] <- ruv_replicate_method(set, k=k)
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177 result[[paste0('control_method_k', k)]] <- ruv_control_gene_method(set, k=k, cutoff_p=0.5)
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178 }
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179
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180 for (name in names(result)) {
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181 if (!startsWith(name, "no_correction")) {
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182 set <- result[[name]]
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183 unwanted_variation <- pData(set)
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184 df <- data.frame(identifier = rownames(unwanted_variation))
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185 df <- cbind(df, unwanted_variation)
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186 colnames(df)[2] <- 'condition'
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187 write.table(df, file=paste0("batch_effects_", name, ".tabular"), sep="\t", quote=F, row.names=F)
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188 }
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189 }
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190
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191 # close the plot device
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192 if (!is.null(opt$plots)) {
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193 cat("closing plot device\n")
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194 dev.off()
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195 }
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196
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197 cat("Session information:\n\n")
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198 sessionInfo()