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annotate ruvseq.R @ 2:fed9d0350d72 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/ruvseq commit 4daa375d022673d2437d609b1865b78c64b04415"
author iuc
date Fri, 15 Jan 2021 17:53:15 +0000
parents c24765926774
children 3a083c78896e
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1 # setup R error handling to go to stderr
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2 library("getopt")
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3 options(show.error.messages = F, error = function() {
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4 cat(geterrmessage(), file = stderr()); q("no", 1, F)
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5 })
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6 options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
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8 setup_cmdline_options <- function() {
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9 args <- commandArgs(trailingOnly = TRUE)
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10 spec <- matrix(c(
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11 "help", "h", 0, "logical",
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12 "alpha", "a", 1, "double",
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13 "min_mean_count", "min_c", 1, "double",
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14 "min_k", "min_k", 1, "double",
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15 "max_k", "max_k", 1, "double",
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16 "sample_json", "s", 1, "character",
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17 "plots", "p", 1, "character",
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18 "header", "H", 0, "logical",
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19 "txtype", "y", 1, "character",
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20 "tx2gene", "x", 1, "character"), # a space-sep tx-to-gene map or GTF file (auto detect .gtf/.GTF)
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21 byrow = TRUE, ncol = 4)
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23 opt <- getopt(spec)
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24 # if help was asked for print a friendly message
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25 # and exit with a non-zero error code
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26 if (!is.null(opt$help)) {
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27 cat(getopt(spec, usage = TRUE))
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28 q(status = 1)
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29 } else {
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30 load_libraries()
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31 }
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32 return(opt)
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33 }
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35 load_libraries <- function() {
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36 # Allows displaying help without waiting for libraries to load
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37 library("tools")
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38 library("jsonlite")
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39 library("reshape2")
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40 library("RUVSeq")
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41 library("RColorBrewer")
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42 library("tximport")
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43 library("DESeq2")
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44 library("ggrepel")
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45 }
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46
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47 source_local <- function(fname) {
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48 argv <- commandArgs(trailingOnly = FALSE)
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49 base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
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50 source(paste(base_dir, fname, sep = "/"))
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51 }
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52
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53 # Source get_deseq_dataset.R for getting deseq dataset from htseq/featurecounts/tximport
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54 source_local("get_deseq_dataset.R")
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56 # RUVseq function definitions
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57
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58 plot_pca_rle <- function(set, title) {
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59 x <- pData(set)[, 1]
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60 colors <- brewer.pal(3, "Set2")
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61 label <- paste0(" for ", title)
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62 plotRLE(set, outline = FALSE, ylim = c(-4, 4), col = colors[x])
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63 title(main = paste0("RLE", label))
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64 plotPCA(set, col = colors[x], cex = 1.2)
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65 title(main = paste0("PCA", label))
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66 }
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67
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68 plot_factors_of_unwanted_var <- function(set, method, k) {
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69 pd <- pData(set)
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70 pd["sample"] <- row.names(pd)
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71 colnames(pd)[1] <- "condition"
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72 d <- melt(pd, id.vars = c("sample", "condition"))
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73 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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74 print(ggplot(d, aes(x = x, y = value, color = condition, label = sample)) +
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75 geom_point() +
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76 ggtitle(paste0("Factors of unwanted variation for method: ", method, ", k=", k)) +
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77 facet_wrap(~ variable, scales = "free_x") +
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78 geom_text_repel() +
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79 theme(axis.title.x = element_blank(),
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80 axis.text.x = element_blank(),
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81 axis.ticks.x = element_blank(),
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82 plot.title = element_text(hjust = 0.5))
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83 )
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84 }
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85
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86 create_seq_expression_set <- function(dds, min_mean_count) {
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87 count_values <- counts(dds)
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88 print(paste0("feature count before filtering :", nrow(count_values), "\n"))
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89 print(paste0("Filtering features which mean expression is less or eq. than ", min_mean_count, " counts\n"))
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90 filter <- apply(count_values, 1, function(x) mean(x) > min_mean_count)
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91 filtered <- count_values[filter, ]
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92 print(paste0("feature count after filtering :", nrow(filtered), "\n"))
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93 set <- newSeqExpressionSet(as.matrix(filtered),
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94 phenoData = data.frame(colData(dds)$condition, row.names = colnames(filtered)))
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95 plot_pca_rle(set = set, title = "raw data")
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96 set <- betweenLaneNormalization(set, which = "upper")
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97 plot_pca_rle(set = set, title = "upper quartile normalized")
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98 return(set)
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99 }
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100
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101 get_empirical_control_genes <- function(set, cutoff_p) {
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102 x <- pData(set)[, 1]
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103 design <- model.matrix(~x, data = pData(set))
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104 y <- DGEList(counts = counts(set), group = x)
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105 y <- calcNormFactors(y, method = "upperquartile")
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106 y <- estimateGLMCommonDisp(y, design)
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107 y <- estimateGLMTagwiseDisp(y, design)
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108 fit <- glmFit(y, design)
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109 lrt <- glmLRT(fit, coef = 2)
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110 top <- topTags(lrt, n = nrow(set))$table
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111 top_rows <- rownames(top)[which(top$PValue < cutoff_p)]
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112 empirical <- rownames(set)[which(!(rownames(set) %in% top_rows))]
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113 return(empirical)
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114 }
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115
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116 ruv_control_gene_method <- function(set, k, control_genes = "empirical", cutoff_p = 0.2) {
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117 if (control_genes == "empirical") {
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118 control_genes <- get_empirical_control_genes(set, cutoff_p = cutoff_p)
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119 }
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120 set <- RUVg(set, control_genes, k = k)
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121 plot_pca_rle(set, paste0("RUVg with empirical control genes, k=", k))
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122 plot_factors_of_unwanted_var(set, method = "RUVg with empirical control genes", k = k)
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123 return(set)
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124 }
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125
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126 ruv_residual_method <- function(set, k) {
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127 genes <- rownames(counts(set))
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128 x <- pData(set)[, 1]
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129 # Initial edger residuals
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130 design <- model.matrix(~x, data = pData(set))
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131 y <- DGEList(counts = counts(set), group = x)
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132 y <- calcNormFactors(y, method = "upperquartile")
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133 y <- estimateGLMCommonDisp(y, design)
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134 y <- estimateGLMTagwiseDisp(y, design)
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135 fit <- glmFit(y, design)
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136 res <- residuals(fit, type = "deviance")
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137 set <- RUVr(set, genes, k = k, res)
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138 plot_pca_rle(set = set, title = paste0("RUVr using residuals, k=", k))
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139 plot_factors_of_unwanted_var(set, method = "RUVr using residuals", k = k)
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140 return(set)
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141 }
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142
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143 ruv_replicate_method <- function(set, k) {
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144 genes <- rownames(counts(set))
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145 x <- pData(set)[, 1]
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146 differences <- makeGroups(x)
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147 set <- RUVs(set, genes, k = k, differences)
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148 plot_pca_rle(set, paste0("RUVs with replicate samples, k=", k))
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149 plot_factors_of_unwanted_var(set, method = "RUVs using replicates", k = k)
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150 return(set)
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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$max_k
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157 min_c <- opt$min_mean_count
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158 sample_json <- fromJSON(opt$sample_json)
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159 sample_paths <- sample_json$path
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160 sample_names <- sample_json$label
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161 condition <- as.factor(sample_json$condition)
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162 sample_table <- data.frame(samplename = sample_names, filename = sample_paths, condition = condition)
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163 rownames(sample_table) <- sample_names
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164
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165 dds <- get_deseq_dataset(sample_table, header = opt$header, design_formula = ~ condition, tximport = opt$txtype, txtype = opt$txtype, tx2gene = opt$tx2gene)
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166 if (!is.null(opt$plots)) {
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167 pdf(opt$plots)
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168 }
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169
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170 # Run through the ruvseq variants
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171 set <- create_seq_expression_set(dds, min_mean_count = min_c)
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172 result <- list(no_correction = set)
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173 for (k in seq(min_k, max_k)) {
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174 result[[paste0("residual_method_k", k)]] <- ruv_residual_method(set, k = k)
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175 result[[paste0("replicate_method_k", k)]] <- ruv_replicate_method(set, k = k)
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176 result[[paste0("control_method_k", k)]] <- ruv_control_gene_method(set, k = k, cutoff_p = 0.5)
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177 }
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178
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179 for (name in names(result)) {
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180 if (!startsWith(name, "no_correction")) {
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181 set <- result[[name]]
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182 unwanted_variation <- pData(set)
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183 df <- data.frame(identifier = rownames(unwanted_variation))
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184 df <- cbind(df, unwanted_variation)
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185 colnames(df)[2] <- "condition"
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186 write.table(df, file = paste0("batch_effects_", name, ".tabular"), sep = "\t", quote = F, row.names = F)
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187 }
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188 }
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189
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190 # close the plot device
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191 if (!is.null(opt$plots)) {
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192 cat("closing plot device\n")
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193 dev.off()
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194 }
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195
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196 cat("Session information:\n\n")
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197 sessionInfo()