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annotate small_rna_maps.r @ 9:3ea75c573429 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 6199193c7fe2cb56403eea8af0b40d44f7311fd5
author artbio
date Tue, 07 Nov 2017 12:31:28 -0500
parents a96e6a7df2b7
children a561a71bd7d7
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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
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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2 options( show.error.messages=F,
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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3 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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a96e6a7df2b7 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 06472d1bd1365e4f7b385d578a69f4646481e22f
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4 options(warn = -1)
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5 library(RColorBrewer)
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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6 library(lattice)
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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7 library(latticeExtra)
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8 library(grid)
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9 library(gridExtra)
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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10 library(optparse)
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12
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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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13 option_list <- list(
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14 make_option(c("-f", "--first_dataframe"), type="character", help="path to first dataframe"),
507383cce5a8 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit edbb53cb13b52bf8e71c562fa8acc2c3be2fb270
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15 make_option(c("-e", "--extra_dataframe"), type="character", help="path to additional dataframe"),
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16 make_option(c("-n", "--normalization"), type="character", help="space-separated normalization/size factors"),
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17 make_option("--first_plot_method", type = "character", help="How additional data should be plotted"),
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18 make_option("--extra_plot_method", type = "character", help="How additional data should be plotted"),
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19 make_option("--global", type = "character", help="data should be plotted as global size distribution"),
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20 make_option("--output_pdf", type = "character", help="path to the pdf file with plots")
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21 )
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23 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
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24 args = parse_args(parser)
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25
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26 # data frames implementation
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27 ## first table
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28 Table = read.delim(args$first_dataframe, header=T, row.names=NULL)
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29 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") {
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30 Table <- within(Table, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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31 }
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6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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32 n_samples=length(unique(Table$Dataset))
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33 samples = unique(Table$Dataset)
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34 if (args$normalization != "") {
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35 norm_factors = as.numeric(unlist(strsplit(args$normalization, " ")))
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36 } else {
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37 norm_factors = rep(1, n_samples)
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38 }
a3be3601bcb3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 9f9c64aff0d225881bedb97bd5035ccbca945d9d
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39 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") {
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40 i = 1
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41 for (sample in samples) {
a3be3601bcb3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 9f9c64aff0d225881bedb97bd5035ccbca945d9d
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42 Table[, length(Table)][Table$Dataset==sample] <- Table[, length(Table)][Table$Dataset==sample]*norm_factors[i]
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43 i = i + 1
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44 }
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45 }
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46 genes=unique(levels(Table$Chromosome))
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47 per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x))
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48 per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) )
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49 n_genes=length(per_gene_readmap)
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50 # second table
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51 if (args$extra_plot_method != '') {
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52 ExtraTable=read.delim(args$extra_dataframe, header=T, row.names=NULL)
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53 if (args$extra_plot_method == "Counts" | args$extra_plot_method=='Size') {
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54 ExtraTable <- within(ExtraTable, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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55 }
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56 if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") {
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57 i = 1
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58 for (sample in samples) {
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59 ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample] <- ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample]*norm_factors[i]
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60 i = i + 1
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61 }
6
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62 }
5
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63 per_gene_size=lapply(genes, function(x) subset(ExtraTable, Chromosome==x))
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64 }
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65
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66 ## functions
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67 globalbc = function(df, global="", ...) {
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68 if (global == "yes") {
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69 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
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70 data = df, origin = 0,
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71 horizontal=FALSE,
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72 col=c("darkblue"),
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73 scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
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74 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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75 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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76 main=title_first_method[[args$first_plot_method]],
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77 layout = c(2, 6), newpage=T,
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78 as.table=TRUE,
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79 aspect=0.5,
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80 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
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81 ...
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82 )
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83 } else {
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84 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
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85 data = df, origin = 0,
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86 horizontal=FALSE,
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87 group=Polarity,
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88 stack=TRUE,
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89 col=c('red', 'blue'),
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90 scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
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91 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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92 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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93 main=title_first_method[[args$first_plot_method]],
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94 layout = c(2, 6), newpage=T,
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95 as.table=TRUE,
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96 aspect=0.5,
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97 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
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98 ...
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99 )
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100 }
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101 return(bc)
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102 }
5
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103 plot_unit = function(df, method=args$first_plot_method, ...) {
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104 if (method == 'Counts') {
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105 p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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106 data=df,
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107 type='h',
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108 lwd=1.5,
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109 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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110 xlab=NULL, main=NULL, ylab=NULL,
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111 as.table=T,
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112 origin = 0,
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113 horizontal=FALSE,
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114 group=Polarity,
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115 col=c("red","blue"),
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116 par.strip.text = list(cex=0.7),
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117 ...)
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118 } else if (method != "Size") {
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119 p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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120 data=df,
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121 type='p',
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122 pch=19,
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123 cex=0.35,
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124 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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125 xlab=NULL, main=NULL, ylab=NULL,
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126 as.table=T,
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127 origin = 0,
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128 horizontal=FALSE,
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129 group=Polarity,
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130 col=c("red","blue"),
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131 par.strip.text = list(cex=0.7),
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132 ...)
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133 } else {
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134 p = barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
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135 horizontal=FALSE,
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136 group=Polarity,
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137 stack=TRUE,
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138 col=c('red', 'blue'),
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139 scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.7), x=list(rot=0, cex=0.7, axs="i", tck=0.5)),
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140 xlab = NULL,
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141 ylab = NULL,
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142 main = NULL,
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143 as.table=TRUE,
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144 par.strip.text = list(cex=0.6),
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145 ...)
0
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146 }
5
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147 combineLimits(p)
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148 }
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149
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150 plot_single <- function(df, method=args$first_plot_method, rows_per_page=rows_per_page, ...) {
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151 if (method == 'Counts') {
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152 p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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153 data=df,
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154 type='h',
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155 lwd=1.5,
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156 scales= list(relation="free", x=list(rot=0, cex=0.7, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)),
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157 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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158 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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159 main=title_first_method[[args$first_plot_method]],
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160 origin = 0,
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161 group=Polarity,
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162 col=c("red","blue"),
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163 par.strip.text = list(cex=0.7),
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164 as.table=T,
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165 ...)
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166 p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
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167 return(p)
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168 } else if (method != "Size") {
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169 p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
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170 data=df,
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171 type='p',
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172 pch=19,
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173 cex=0.35,
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174 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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175 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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176 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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177 main=title_first_method[[args$first_plot_method]],
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178 origin = 0,
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179 group=Polarity,
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180 col=c("red","blue"),
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181 par.strip.text = list(cex=0.7),
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182 as.table=T,
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183 ...)
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184 p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
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185 return(p)
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186 } else {
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187 p= barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
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188 horizontal=FALSE,
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189 group=Polarity,
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190 stack=TRUE,
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191 col=c('red', 'blue'),
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192 scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
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193 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
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194 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
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195 main=title_first_method[[args$first_plot_method]],
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196 par.strip.text = list(cex=0.7),
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197 nrow = 8,
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198 as.table=TRUE,
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199 ...)
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200 p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
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201 p = combineLimits(p, extend=TRUE)
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202 return (p)
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203 }
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204 }
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205
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206 ## function parameters
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207
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208 #par.settings.firstplot = list(layout.heights=list(top.padding=11, bottom.padding = -14))
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209 #par.settings.secondplot=list(layout.heights=list(top.padding=11, bottom.padding = -15), strip.background=list(col=c("lavender","deepskyblue")))
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210 par.settings.firstplot = list(layout.heights=list(top.padding=-2, bottom.padding=-2))
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211 par.settings.secondplot=list(layout.heights=list(top.padding=-1, bottom.padding=-1), strip.background=list(col=c("lavender","deepskyblue")))
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212 par.settings.single_plot=list(strip.background = list(col = c("lightblue", "lightgreen")))
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213 title_first_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
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214 title_extra_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
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215 legend_first_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
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216 legend_extra_method =list(Counts="Read count", Coverage="Coveragedepth", Median="Median size", Mean="Mean size", Size="Read count")
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217 bottom_first_method =list(Counts="Coordinates (nbre of bases)",Coverage="Coordinates (nbre of bases)", Median="Coordinates (nbre of bases)", Mean="Coordinates (nbre of bases)", Size="Sizes of reads")
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218 bottom_extra_method =list(Counts="Coordinates (nbre of bases)",Coverage="Coordinates (nbre of bases)", Median="Coordinates (nbre of bases)", Mean="Coordinates (nbre of bases)", Size="Sizes of reads")
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219
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220 ## Plotting Functions
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221
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222 double_plot <- function(...) {
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223 if (n_genes > 5) {page_height=15; rows_per_page=10} else {
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224 rows_per_page= 2 * n_genes; page_height=1.5*n_genes}
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225 if (n_samples > 4) {page_width = 8.2677*n_samples/4} else {page_width = 7 * n_samples/2}
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226 pdf(file=args$output_pdf, paper="special", height=page_height, width=page_width)
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227 for (i in seq(1,n_genes,rows_per_page/2)) {
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228 start=i
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229 end=i+rows_per_page/2-1
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230 if (end>n_genes) {end=n_genes}
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231 first_plot.list = lapply(per_gene_readmap[start:end], function(x) plot_unit(x, strip=FALSE, par.settings=par.settings.firstplot))
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232 second_plot.list = lapply(per_gene_size[start:end], function(x) plot_unit(x, method=args$extra_plot_method, par.settings=par.settings.secondplot))
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233 plot.list=rbind(second_plot.list, first_plot.list)
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234 args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(c(40,30,40,30,40,30,40,30,40,30,10), rep("mm", 11)),
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235 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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236 left=textGrob(paste(legend_first_method[[args$first_plot_method]], "/", legend_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), vjust=2, rot=90),
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237 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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238 )
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239 )
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240 do.call(grid.arrange, args_list)
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241 }
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242 devname=dev.off()
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243 }
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245
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246 single_plot <- function(...) {
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247 width = 8.2677 * n_samples / 2
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248 rows_per_page=8
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249 pdf(file=args$output_pdf, paper="special", height=11.69, width=width)
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250 for (i in seq(1,n_genes,rows_per_page)) {
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251 start=i
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252 end=i+rows_per_page-1
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253 if (end>n_genes) {end=n_genes}
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254 bunch = do.call(rbind, per_gene_readmap[start:end]) # sub dataframe from the list
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255 p = plot_single(bunch, method=args$first_plot_method, par.settings=par.settings.single_plot, rows_per_page=rows_per_page)
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256 plot(p)
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257 }
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258 devname=dev.off()
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259 }
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260
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261 # main
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262
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263 if (args$extra_plot_method != '') { double_plot() }
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264 if (args$extra_plot_method == '' & !exists('global', where=args)) {
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265 single_plot()
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266 }
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267 if (exists('global', where=args)) {
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268 pdf(file=args$output, paper="special", height=11.69)
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269 Table <- within(Table, Counts[Polarity=="R"] <- abs(Counts[Polarity=="R"])) # retropedalage
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270 library(reshape2)
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271 ml = melt(Table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
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272 if (args$global == "nomerge") {
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273 castml = dcast(ml, Dataset+Polarity+Size ~ variable, function(x) sum(x))
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274 castml <- within(castml, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
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275 bc = globalbc(castml, global="no")
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276 } else {
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277 castml = dcast(ml, Dataset+Size ~ variable, function(x) sum(x))
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278 bc = globalbc(castml, global="yes")
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279 }
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280 plot(bc)
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281 devname=dev.off()
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282 }
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283
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