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annotate small_rna_maps.r @ 30:183bf49fe77c draft

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"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d280e9be7cf96f4938a73ccf5985533109f3328f"
author artbio
date Sat, 05 Oct 2019 18:25:19 -0400
parents fe1a9cfaf5c3
children f2e7ad3058e8
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1 ## Setup R error handling to go to stderr
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2 options( show.error.messages=F,
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3 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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4 options(warn = -1)
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5 library(RColorBrewer)
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6 library(lattice)
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7 library(latticeExtra)
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8 library(grid)
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9 library(gridExtra)
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10 library(optparse)
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13 option_list <- list(
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14 make_option(c("-i", "--ymin"), type="double", help="set min ylimit. e.g. '-100.0'"),
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15 make_option(c("-a", "--ymax"), type="double", help="set max ylimit. e.g. '100.0'"),
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16 make_option(c("-f", "--first_dataframe"), type="character", help="path to first dataframe"),
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17 make_option(c("-e", "--extra_dataframe"), type="character", help="path to additional dataframe"),
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18 make_option(c("-n", "--normalization"), type="character", help="space-separated normalization/size factors"),
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19 make_option("--first_plot_method", type = "character", help="How additional data should be plotted"),
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20 make_option("--extra_plot_method", type = "character", help="How additional data should be plotted"),
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21 make_option("--global", type = "character", help="data should be plotted as global size distribution"),
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22 make_option("--output_pdf", type = "character", help="path to the pdf file with plots")
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23 )
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25 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
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26 args = parse_args(parser)
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28 # data frames implementation
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30 ## first table
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31 Table = read.delim(args$first_dataframe, header=T, row.names=NULL)
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32 colnames(Table)[1] <- "Dataset"
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33 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
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34 Table <- Table[,!(names(Table) %in% dropcol)]
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35 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") {
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36 Table <- within(Table, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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37 }
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38 n_samples=length(unique(Table$Dataset))
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39 samples = unique(Table$Dataset)
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40 if (args$normalization != "") {
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41 norm_factors = as.numeric(unlist(strsplit(args$normalization, " ")))
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42 } else {
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43 norm_factors = rep(1, n_samples)
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44 }
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45 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") {
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46 i = 1
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47 for (sample in samples) {
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48 # Warning
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49 # Here the column is hard coded as the last column (dangerous)
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50 # because its name changes with the method
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51 Table[, length(Table)][Table$Dataset==sample] <- Table[, length(Table)][Table$Dataset==sample]*norm_factors[i]
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52 i = i + 1
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53 }
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54 }
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55 genes=unique(Table$Chromosome)
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56 per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x))
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57 per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) )
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58 n_genes=length(per_gene_readmap)
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59
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60 # second table
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61 if (args$extra_plot_method != '') {
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62 ExtraTable=read.delim(args$extra_dataframe, header=T, row.names=NULL)
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63 colnames(ExtraTable)[1] <- "Dataset"
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64 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
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65 Table <- Table[,!(names(Table) %in% dropcol)]
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66 if (args$extra_plot_method == "Counts" | args$extra_plot_method=='Size') {
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67 ExtraTable <- within(ExtraTable, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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68 }
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69 if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") {
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70 i = 1
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71 for (sample in samples) {
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72 ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample] <- ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample]*norm_factors[i]
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73 i = i + 1
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74 }
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75 }
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76 per_gene_size=lapply(genes, function(x) subset(ExtraTable, Chromosome==x))
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77 }
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79 ## functions
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80 globalbc = function(df, global="", ...) {
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81 if (global == "yes") {
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82 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
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83 data = df, origin = 0,
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84 horizontal=FALSE,
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85 col=c("darkblue"),
21
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86 scales=list(y=list(tick.number=4, rot=90, relation="same", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
12
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87 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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88 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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89 main=title_first_method[[args$first_plot_method]],
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90 layout = c(2, 6), newpage=T,
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91 as.table=TRUE,
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92 aspect=0.5,
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93 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
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94 ...
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95 )
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96 } else {
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97 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
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98 data = df, origin = 0,
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99 horizontal=FALSE,
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100 group=Polarity,
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101 stack=TRUE,
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102 col=c('red', 'blue'),
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103 scales=list(y=list(tick.number=4, rot=90, relation="same", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
12
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104 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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105 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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106 main=title_first_method[[args$first_plot_method]],
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107 layout = c(2, 6), newpage=T,
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108 as.table=TRUE,
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109 aspect=0.5,
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110 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
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111 ...
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112 )
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113 }
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114 return(bc)
7
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115 }
5
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116 plot_unit = function(df, method=args$first_plot_method, ...) {
12
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117 if (exists('ymin', where=args)){
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118 min=args$ymin
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119 }else{
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120 min=''
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121 }
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122 if ((exists('ymax', where=args))){
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123 max=args$ymax
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124 }else{
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diff changeset
125 max=''
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126 }
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127 ylimits=c(min,max)
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128 if (method == 'Counts') {
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129 p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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130 data=df,
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131 type='h',
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132 lwd=1.5,
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133 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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134 xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits,
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135 as.table=T,
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136 origin = 0,
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137 horizontal=FALSE,
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diff changeset
138 group=Polarity,
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139 col=c("red","blue"),
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140 par.strip.text = list(cex=0.7),
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141 ...)
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142 p=combineLimits(p)
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143 } else if (method != "Size") {
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144 p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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145 data=df,
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146 type= ifelse(method=='Coverage', 'l', 'p'),
12
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147 pch=19,
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148 cex=0.35,
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149 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)),
12
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150 xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits,
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diff changeset
151 as.table=T,
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diff changeset
152 origin = 0,
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diff changeset
153 horizontal=FALSE,
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diff changeset
154 group=Polarity,
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diff changeset
155 col=c("red","blue"),
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diff changeset
156 par.strip.text = list(cex=0.7),
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diff changeset
157 ...)
30
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diff changeset
158 p=combineLimits(p)
12
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diff changeset
159 } else {
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160 p = barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
d33263e6e812 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit b0676fd329c2ca50002f9f2fede531d8e550569f
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diff changeset
161 horizontal=FALSE,
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diff changeset
162 group=Polarity,
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diff changeset
163 stack=TRUE,
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diff changeset
164 col=c('red', 'blue'),
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165 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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166 xlab = NULL,
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167 ylab = NULL,
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168 main = NULL,
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169 as.table=TRUE,
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170 par.strip.text = list(cex=0.6),
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171 ...)
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172 p=combineLimits(p)
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173 }
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174 return(p)
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175 }
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176
12
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177
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178 ## function parameters
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179
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180 #par.settings.firstplot = list(layout.heights=list(top.padding=11, bottom.padding = -14))
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181 #par.settings.secondplot=list(layout.heights=list(top.padding=11, bottom.padding = -15), strip.background=list(col=c("lavender","deepskyblue")))
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182 par.settings.firstplot = list(layout.heights=list(top.padding=-2, bottom.padding=-2),strip.background=list(col=c("lightblue","lightgreen")))
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183 par.settings.secondplot=list(layout.heights=list(top.padding=-1, bottom.padding=-1),strip.background=list(col=c("lightblue","lightgreen")))
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184 title_first_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
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185 title_extra_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
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186 legend_first_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
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187 legend_extra_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
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188 bottom_first_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
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189 bottom_extra_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
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190
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191 ## Plotting Functions
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192
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193 double_plot <- function(...) {
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194 page_height = 15
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195 rows_per_page = 10
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196 graph_heights=c(40,30,40,30,40,30,40,30,40,30,10)
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197 page_width=8.2677 * n_samples / 2
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198 pdf(file=args$output_pdf, paper="special", height=page_height, width=page_width)
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199 for (i in seq(1,n_genes,rows_per_page/2)) {
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200 start=i
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201 end=i+rows_per_page/2-1
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202 if (end>n_genes) {end=n_genes}
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203 if (end-start+1 < 5) {graph_heights=c(rep(c(40,30),end-start+1),10,rep(c(40,30),5-(end-start+1)))}
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204 first_plot.list = lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings=par.settings.secondplot), strip.left=strip.custom(par.strip.text = list(cex=0.5)))))
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205 second_plot.list = lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method=args$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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206 plot.list=rbind(first_plot.list, second_plot.list)
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207 args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 11)),
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208 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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209 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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210 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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211 )
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212 )
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213 do.call(grid.arrange, args_list)
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214 }
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215 devname=dev.off()
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216 }
0
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217
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218
5
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219 single_plot <- function(...) {
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220 width = 8.2677 * n_samples / 2
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221 rows_per_page=8
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222 graph_heights=c(rep(40,8),10)
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223 pdf(file=args$output_pdf, paper="special", height=15, width=width)
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224 for (i in seq(1,n_genes,rows_per_page)) {
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225 start=i
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226 end=i+rows_per_page-1
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227 if (end>n_genes) {end=n_genes}
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228 if (end-start+1 < 8) {graph_heights=c(rep(c(40),end-start+1),10,rep(c(40),8-(end-start+1)))}
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229 first_plot.list = lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings=par.settings.firstplot),strip.left=strip.custom(par.strip.text = list(cex=0.5)))))
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230 plot.list=rbind(first_plot.list)
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231 args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 9)),
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232 top=textGrob(title_first_method[[args$first_plot_method]], gp=gpar(cex=1), vjust=0, just="top"),
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233 left=textGrob(legend_first_method[[args$first_plot_method]], gp=gpar(cex=1), vjust=0, hjust=0, x=1, y=(-0.41/7)*(end-start-(6.23/0.41)), rot=90),
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234 sub=textGrob(bottom_first_method[[args$first_plot_method]], gp=gpar(cex=1), just="bottom", vjust=2)
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235 )
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236 )
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237 do.call(grid.arrange, args_list)
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238 }
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239 devname=dev.off()
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240 }
0
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241
5
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242 # main
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243
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244 if (args$extra_plot_method != '') { double_plot() }
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245 if (args$extra_plot_method == '' & !exists('global', where=args)) {
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246 single_plot()
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247 }
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248 if (exists('global', where=args)) {
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249 pdf(file=args$output, paper="special", height=11.69)
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250 Table <- within(Table, Counts[Polarity=="R"] <- abs(Counts[Polarity=="R"])) # retropedalage
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251 library(reshape2)
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252 ml = melt(Table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
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253 if (args$global == "nomerge") {
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254 castml = dcast(ml, Dataset+Polarity+Size ~ variable, function(x) sum(x))
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255 castml <- within(castml, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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256 bc = globalbc(castml, global="no")
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257 } else {
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258 castml = dcast(ml, Dataset+Size ~ variable, function(x) sum(x))
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259 bc = globalbc(castml, global="yes")
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260 }
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261 plot(bc)
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262 devname=dev.off()
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263 }
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264