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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit b7c7c60d608694ca4f1638e4bb0d6db5b1afa944
author artbio
date Fri, 21 Oct 2022 23:19:55 +0000
parents f2e7ad3058e8
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6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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1 ## Setup R error handling to go to stderr
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2 options(show.error.messages = FALSE,
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3 error = function() {
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4 cat(geterrmessage(), file = stderr())
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5 q("no", 1, FALSE)
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6 }
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7 )
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8 options(warn = -1)
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9 library(RColorBrewer)
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10 library(lattice)
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11 library(latticeExtra)
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12 library(grid)
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13 library(gridExtra)
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14 library(optparse)
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17 option_list <- list(
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18 make_option(c("-i", "--ymin"), type = "double", help = "set min ylimit. e.g. '-100.0'"),
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19 make_option(c("-a", "--ymax"), type = "double", help = "set max ylimit. e.g. '100.0'"),
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20 make_option(c("-f", "--first_dataframe"), type = "character", help = "path to first dataframe"),
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21 make_option(c("-e", "--extra_dataframe"), type = "character", help = "path to additional dataframe"),
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22 make_option(c("-n", "--normalization"), type = "character", help = "space-separated normalization/size factors"),
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23 make_option("--first_plot_method", type = "character", help = "How additional data should be plotted"),
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24 make_option("--extra_plot_method", type = "character", help = "How additional data should be plotted"),
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25 make_option("--global", type = "character", help = "data should be plotted as global size distribution"),
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26 make_option("--output_pdf", type = "character", help = "path to the pdf file with plots")
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27 )
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29 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
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30 args <- parse_args(parser)
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32 # data frames implementation
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34 ## first table
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35 table <- read.delim(args$first_dataframe, header = TRUE, row.names = NULL)
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36 colnames(table)[1] <- "Dataset"
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37 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
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38 table <- table[, !(names(table) %in% dropcol)]
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39 if (args$first_plot_method == "Counts" || args$first_plot_method == "Size") {
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40 table <- within(table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * - 1)) # nolint
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41 }
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42 n_samples <- length(unique(table$Dataset))
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43 samples <- unique(table$Dataset)
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44 if (args$normalization != "") {
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45 norm_factors <- as.numeric(unlist(strsplit(args$normalization, " ")))
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46 } else {
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47 norm_factors <- rep(1, n_samples)
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48 }
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49 if (args$first_plot_method == "Counts" || args$first_plot_method == "Size" || args$first_plot_method == "Coverage") {
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50 i <- 1
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51 for (sample in samples) {
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52 # Warning Here the column is hard coded as the last column (dangerous)
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53 # because its name changes with the method
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54 table[, length(table)][table$Dataset == sample] <- table[, length(table)][table$Dataset == sample] * norm_factors[i]
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55 i <- i + 1
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56 }
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57 }
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58 genes <- unique(table$Chromosome)
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59 per_gene_readmap <- lapply(genes, function(x) subset(table, Chromosome == x))
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60 per_gene_limit <- lapply(genes, function(x) c(1, unique(subset(table, Chromosome == x)$Chrom_length)))
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61 n_genes <- length(per_gene_readmap)
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63 # second table
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64 if (args$extra_plot_method != "") {
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65 extra_table <- read.delim(args$extra_dataframe, header = TRUE, row.names = NULL)
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66 colnames(extra_table)[1] <- "Dataset"
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67 dropcol <- c("Strandness", "z.score")
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68 table <- table[, !(names(table) %in% dropcol)]
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69 if (args$extra_plot_method == "Counts" || args$extra_plot_method == "Size") {
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70 extra_table <- within(extra_table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1)) # nolint
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71 }
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72 if (args$extra_plot_method == "Counts" || args$extra_plot_method == "Size" || args$extra_plot_method == "Coverage") {
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73 i <- 1
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74 for (sample in samples) {
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75 extra_table[, length(extra_table)][extra_table$Dataset == sample] <- extra_table[, length(extra_table)][extra_table$Dataset == sample] * norm_factors[i]
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76 i <- i + 1
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77 }
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78 }
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79 per_gene_size <- lapply(genes, function(x) subset(extra_table, Chromosome == x))
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80 }
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81
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82 ## functions
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83 globalbc <- function(df, global = "", ...) {
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84 if (global == "yes") {
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85 bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
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86 data = df, origin = 0,
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87 horizontal = FALSE,
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88 col = c("darkblue"),
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89 scales = list(y = list(tick.number = 4, rot = 90, relation = "same", cex = 0.5, alternating = TRUE), x = list(rot = 0, cex = 0.6, tck = 0.5, alternating = c(3, 3))),
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90 xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
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91 ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
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92 main = title_first_method[[args$first_plot_method]],
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93 layout = c(2, 6), newpage = TRUE,
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94 as.table = TRUE,
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95 aspect = 0.5,
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96 strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
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97 ...
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98 )
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99 } else {
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100 bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
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101 data = df, origin = 0,
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102 horizontal = FALSE,
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103 group = Polarity,
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104 stack = TRUE,
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105 col = c("red", "blue"),
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106 scales = list(y = list(tick.number = 4, rot = 90, relation = "same", cex = 0.5, alternating = TRUE), x = list(rot = 0, cex = 0.6, tck = 0.5, alternating = c(3, 3))),
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107 xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
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108 ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
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109 main = title_first_method[[args$first_plot_method]],
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110 layout = c(2, 6), newpage = TRUE,
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111 as.table = TRUE,
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112 aspect = 0.5,
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113 strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
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114 ...
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115 )
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116 }
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117 return(bc)
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118 }
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119 plot_unit <- function(df, method = args$first_plot_method, ...) {
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120 if (exists("ymin", where = args)) {
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121 min <- args$ymin
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122 } else {
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123 min <- ""
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124 }
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125 if ((exists("ymax", where = args))) {
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126 max <- args$ymax
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127 } else {
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128 max <- ""
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129 }
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130 ylimits <- c(min, max)
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131 if (method == "Counts") {
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132 p <- xyplot(Counts ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
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133 data = df,
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134 type = "h",
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135 lwd = 1.5,
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136 scales = list(relation = "free", x = list(rot = 0, cex = 0.7, axs = "i", tck = 0.5), y = list(tick.number = 4, rot = 90, cex = 0.7)),
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137 xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
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138 as.table = TRUE,
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139 origin = 0,
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140 horizontal = FALSE,
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141 group = Polarity,
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142 col = c("red", "blue"),
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143 par.strip.text = list(cex = 0.7),
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144 ...)
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145 p <- combineLimits(p)
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146 } else if (method != "Size") {
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147 p <- xyplot(eval(as.name(method)) ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
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148 data = df,
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149 type = ifelse(method == "Coverage", "l", "p"),
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150 pch = 19,
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151 cex = 0.35,
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152 scales = list(relation = "free", x = list(rot = 0, cex = 0.7, axs = "i", tck = 0.5), y = list(tick.number = 4, rot = 90, cex = 0.7)),
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153 xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
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154 as.table = TRUE,
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155 origin = 0,
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156 horizontal = FALSE,
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157 group = Polarity,
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158 col = c("red", "blue"),
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159 par.strip.text = list(cex = 0.7),
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160 ...)
32
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161 p <- combineLimits(p)
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162 } else {
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163 p <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)) + Chromosome, data = df, origin = 0,
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164 horizontal = FALSE,
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165 group = Polarity,
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166 stack = TRUE,
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167 col = c("red", "blue"),
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168 scales = list(y = list(rot = 90, relation = "free", cex = 0.7), x = list(rot = 0, cex = 0.7, axs = "i", tck = c(1, 0))),
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169 xlab = NULL,
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170 ylab = NULL,
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171 main = NULL,
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172 as.table = TRUE,
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173 par.strip.text = list(cex = 0.6),
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174 ...)
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175 p <- combineLimits(p)
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176 }
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177 return(p)
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178 }
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179
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180
5
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181 ## function parameters
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182
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183 par_settings_firstplot <- list(layout.heights = list(top.padding = -2, bottom.padding = -2), strip.background = list(col = c("lightblue", "lightgreen")))
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184 par_settings_secondplot <- list(layout.heights = list(top.padding = -1, bottom.padding = -1), strip.background = list(col = c("lightblue", "lightgreen")))
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185 title_first_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
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186 title_extra_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
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187 legend_first_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
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188 legend_extra_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
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189 bottom_first_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
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190 bottom_extra_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
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191
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192 ## Plotting Functions
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193
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194 double_plot <- function(...) {
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195 page_height <- 15
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196 rows_per_page <- 10
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197 graph_heights <- c(40, 30, 40, 30, 40, 30, 40, 30, 40, 30, 10)
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198 page_width <- 8.2677 * n_samples / 2
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199 pdf(file = args$output_pdf, paper = "special", height = page_height, width = page_width)
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200 for (i in seq(1, n_genes, rows_per_page / 2)) {
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201 start <- i
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202 end <- i + rows_per_page / 2 - 1
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203 if (end > n_genes) {
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204 end <- n_genes
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205 }
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206 if (end - start + 1 < 5) {
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207 graph_heights <- c(rep(c(40, 30), end - start + 1), 10, rep(c(40, 30), 5 - (end - start + 1)))
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208 }
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209 first_plot_list <- lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings = par_settings_secondplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)))))
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210 second_plot_list <- lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method = args$extra_plot_method, par.settings = par_settings_firstplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)), strip = FALSE)))
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211 plot.list <- rbind(first_plot_list, second_plot_list)
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212 args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 11)),
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213 top = textGrob(paste(title_first_method[[args$first_plot_method]], "and", title_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), vjust = 0, just = "top"),
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214 left = textGrob(paste(legend_first_method[[args$first_plot_method]], "/", legend_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), vjust = 0, hjust = 0, x = 1, y = (-0.38 / 4) * (end - start - (3.28 / 0.38)), rot = 90),
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215 sub = textGrob(paste(bottom_first_method[[args$first_plot_method]], "/", bottom_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), just = "bottom", vjust = 2)
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216 )
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217 )
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218 do.call(grid.arrange, args_list)
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219 }
32
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220 devname <- dev.off()
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221 }
0
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222
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223
5
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224 single_plot <- function(...) {
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225 width <- 8.2677 * n_samples / 2
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226 rows_per_page <- 8
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227 graph_heights <- c(rep(40, 8), 10)
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228 pdf(file = args$output_pdf, paper = "special", height = 15, width = width)
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229 for (i in seq(1, n_genes, rows_per_page)) {
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diff changeset
230 start <- i
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231 end <- i + rows_per_page - 1
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232 if (end > n_genes) {
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233 end <- n_genes
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234 }
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235 if (end - start + 1 < 8) {
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236 graph_heights <- c(rep(c(40), end - start + 1), 10, rep(c(40), 8 - (end - start + 1)))
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237 }
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238 first_plot_list <- lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings = par_settings_firstplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)))))
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239 plot.list <- rbind(first_plot_list)
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240 args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 9)),
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241 top = textGrob(title_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, just = "top"),
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242 left = textGrob(legend_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, hjust = 0, x = 1, y = (-0.41 / 7) * (end - start - (6.23 / 0.41)), rot = 90),
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243 sub = textGrob(bottom_first_method[[args$first_plot_method]], gp = gpar(cex = 1), just = "bottom", vjust = 2)
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244 )
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245 )
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246 do.call(grid.arrange, args_list)
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247 }
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248 devname <- dev.off()
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249 }
0
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250
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251 # main
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252
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253 if (args$extra_plot_method != "") {
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254 double_plot()
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255 }
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256 if (args$extra_plot_method == "" && !exists("global", where = args)) {
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257 single_plot()
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258 }
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259 if (exists("global", where = args)) {
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260 pdf(file = args$output, paper = "special", height = 11.69)
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261 table <- within(table, Counts[Polarity == "R"] <- abs(Counts[Polarity == "R"])) # nolint
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262 library(reshape2)
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263 ml <- melt(table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
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264 if (args$global == "nomerge") {
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265 castml <- dcast(ml, Dataset + Polarity + Size ~ variable, function(x) sum(x))
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266 castml <- within(castml, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1)) # nolint
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267 bc <- globalbc(castml, global = "no")
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268 } else {
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269 castml <- dcast(ml, Dataset + Size ~ variable, function(x) sum(x))
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270 bc <- globalbc(castml, global = "yes")
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271 }
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272 plot(bc)
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273 devname <- dev.off()
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274 }