Mercurial > repos > artbio > small_rna_maps

comparison small_rna_maps.r @ 32:f2e7ad3058e8 draft

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"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 51dc6c56c7d95fc229ffee958354211cd454fd36"
author artbio
date Sun, 09 May 2021 17:11:00 +0000
parents 183bf49fe77c
children 966bc5c46efd
comparison
equal deleted inserted replaced
31:f82badb66c34 32:f2e7ad3058e8
1 ## Setup R error handling to go to stderr 1 ## Setup R error handling to go to stderr
2 options( show.error.messages=F, 2 options(show.error.messages = F,
3 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) 3 error = function() {
4 cat(geterrmessage(), file = stderr()); q("no", 1, F)
5 }
6 )
4 options(warn = -1) 7 options(warn = -1)
5 library(RColorBrewer) 8 library(RColorBrewer)
6 library(lattice) 9 library(lattice)
7 library(latticeExtra) 10 library(latticeExtra)
8 library(grid) 11 library(grid)
9 library(gridExtra) 12 library(gridExtra)
10 library(optparse) 13 library(optparse)
11 14
12 15
13 option_list <- list( 16 option_list <- list(
14 make_option(c("-i", "--ymin"), type="double", help="set min ylimit. e.g. '-100.0'"), 17 make_option(c("-i", "--ymin"), type = "double", help = "set min ylimit. e.g. '-100.0'"),
15 make_option(c("-a", "--ymax"), type="double", help="set max ylimit. e.g. '100.0'"), 18 make_option(c("-a", "--ymax"), type = "double", help = "set max ylimit. e.g. '100.0'"),
16 make_option(c("-f", "--first_dataframe"), type="character", help="path to first dataframe"), 19 make_option(c("-f", "--first_dataframe"), type = "character", help = "path to first dataframe"),
17 make_option(c("-e", "--extra_dataframe"), type="character", help="path to additional dataframe"), 20 make_option(c("-e", "--extra_dataframe"), type = "character", help = "path to additional dataframe"),
18 make_option(c("-n", "--normalization"), type="character", help="space-separated normalization/size factors"), 21 make_option(c("-n", "--normalization"), type = "character", help = "space-separated normalization/size factors"),
19 make_option("--first_plot_method", type = "character", help="How additional data should be plotted"), 22 make_option("--first_plot_method", type = "character", help = "How additional data should be plotted"),
20 make_option("--extra_plot_method", type = "character", help="How additional data should be plotted"), 23 make_option("--extra_plot_method", type = "character", help = "How additional data should be plotted"),
21 make_option("--global", type = "character", help="data should be plotted as global size distribution"), 24 make_option("--global", type = "character", help = "data should be plotted as global size distribution"),
22 make_option("--output_pdf", type = "character", help="path to the pdf file with plots") 25 make_option("--output_pdf", type = "character", help = "path to the pdf file with plots")
23 ) 26 )
24 27
25 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list) 28 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
26 args = parse_args(parser) 29 args <- parse_args(parser)
27 30
28 # data frames implementation 31 # data frames implementation
29 32
30 ## first table 33 ## first table
31 Table = read.delim(args$first_dataframe, header=T, row.names=NULL) 34 table <- read.delim(args$first_dataframe, header = T, row.names = NULL)
32 colnames(Table)[1] <- "Dataset" 35 colnames(table)[1] <- "Dataset"
33 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility 36 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
34 Table <- Table[,!(names(Table) %in% dropcol)] 37 table <- table[, !(names(table) %in% dropcol)]
35 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") { 38 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") {
36 Table <- within(Table, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1)) 39 table <- within(table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * - 1))
37 } 40 }
38 n_samples=length(unique(Table$Dataset)) 41 n_samples <- length(unique(table$Dataset))
39 samples = unique(Table$Dataset) 42 samples <- unique(table$Dataset)
40 if (args$normalization != "") { 43 if (args$normalization != "") {
41 norm_factors = as.numeric(unlist(strsplit(args$normalization, " "))) 44 norm_factors <- as.numeric(unlist(strsplit(args$normalization, " ")))
42 } else { 45 } else {
43 norm_factors = rep(1, n_samples) 46 norm_factors <- rep(1, n_samples)
44 } 47 }
45 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") { 48 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") {
46 i = 1 49 i <- 1
47 for (sample in samples) { 50 for (sample in samples) {
48 # Warning 51 # Warning Here the column is hard coded as the last column (dangerous)
49 # Here the column is hard coded as the last column (dangerous)
50 # because its name changes with the method 52 # because its name changes with the method
51 Table[, length(Table)][Table$Dataset==sample] <- Table[, length(Table)][Table$Dataset==sample]*norm_factors[i] 53 table[, length(table)][table$Dataset == sample] <- table[, length(table)][table$Dataset == sample] * norm_factors[i]
52 i = i + 1 54 i <- i + 1
53 } 55 }
54 } 56 }
55 genes=unique(Table$Chromosome) 57 genes <- unique(table$Chromosome)
56 per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x)) 58 per_gene_readmap <- lapply(genes, function(x) subset(table, Chromosome == x))
57 per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) ) 59 per_gene_limit <- lapply(genes, function(x) c(1, unique(subset(table, Chromosome == x)$Chrom_length)))
58 n_genes=length(per_gene_readmap) 60 n_genes <- length(per_gene_readmap)
59 61
60 # second table 62 # second table
61 if (args$extra_plot_method != '') { 63 if (args$extra_plot_method != "") {
62 ExtraTable=read.delim(args$extra_dataframe, header=T, row.names=NULL) 64 extra_table <- read.delim(args$extra_dataframe, header = T, row.names = NULL)
63 colnames(ExtraTable)[1] <- "Dataset" 65 colnames(extra_table)[1] <- "Dataset"
64 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility 66 dropcol <- c("Strandness", "z.score")
65 Table <- Table[,!(names(Table) %in% dropcol)] 67 table <- table[, !(names(table) %in% dropcol)]
66 if (args$extra_plot_method == "Counts" | args$extra_plot_method=='Size') { 68 if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size") {
67 ExtraTable <- within(ExtraTable, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1)) 69 extra_table <- within(extra_table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1))
68 } 70 }
69 if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") { 71 if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") {
70 i = 1 72 i <- 1
71 for (sample in samples) { 73 for (sample in samples) {
72 ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample] <- ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample]*norm_factors[i] 74 extra_table[, length(extra_table)][extra_table$Dataset == sample] <- extra_table[, length(extra_table)][extra_table$Dataset == sample] * norm_factors[i]
73 i = i + 1 75 i <- i + 1
74 } 76 }
75 } 77 }
76 per_gene_size=lapply(genes, function(x) subset(ExtraTable, Chromosome==x)) 78 per_gene_size <- lapply(genes, function(x) subset(extra_table, Chromosome == x))
77 } 79 }
78 80
79 ## functions 81 ## functions
80 globalbc = function(df, global="", ...) { 82 globalbc <- function(df, global = "", ...) {
81 if (global == "yes") { 83 if (global == "yes") {
82 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)), 84 bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
83 data = df, origin = 0, 85 data = df, origin = 0,
84 horizontal=FALSE, 86 horizontal = FALSE,
85 col=c("darkblue"), 87 col = c("darkblue"),
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))), 88 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))),
87 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85), 89 xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
88 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85), 90 ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
89 main=title_first_method[[args$first_plot_method]], 91 main = title_first_method[[args$first_plot_method]],
90 layout = c(2, 6), newpage=T, 92 layout = c(2, 6), newpage = T,
91 as.table=TRUE, 93 as.table = TRUE,
92 aspect=0.5, 94 aspect = 0.5,
93 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"), 95 strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
94 ... 96 ...
95 ) 97 )
96 } else { 98 } else {
97 bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)), 99 bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
98 data = df, origin = 0, 100 data = df, origin = 0,
99 horizontal=FALSE, 101 horizontal = FALSE,
100 group=Polarity, 102 group = Polarity,
101 stack=TRUE, 103 stack = TRUE,
102 col=c('red', 'blue'), 104 col = c("red", "blue"),
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))), 105 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))),
104 xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85), 106 xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
105 ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85), 107 ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
106 main=title_first_method[[args$first_plot_method]], 108 main = title_first_method[[args$first_plot_method]],
107 layout = c(2, 6), newpage=T, 109 layout = c(2, 6), newpage = T,
108 as.table=TRUE, 110 as.table = TRUE,
109 aspect=0.5, 111 aspect = 0.5,
110 strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"), 112 strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
111 ... 113 ...
112 ) 114 )
113 } 115 }
114 return(bc) 116 return(bc)
115 } 117 }
116 plot_unit = function(df, method=args$first_plot_method, ...) { 118 plot_unit <- function(df, method = args$first_plot_method, ...) {
117 if (exists('ymin', where=args)){ 119 if (exists("ymin", where = args)) {
118 min=args$ymin 120 min <- args$ymin
119 }else{ 121 } else {
120 min='' 122 min <- ""
121 } 123 }
122 if ((exists('ymax', where=args))){ 124 if ((exists("ymax", where = args))) {
123 max=args$ymax 125 max <- args$ymax
124 }else{ 126 } else {
125 max='' 127 max <- ""
126 } 128 }
127 ylimits=c(min,max) 129 ylimits <- c(min, max)
128 if (method == 'Counts') { 130 if (method == "Counts") {
129 p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), 131 p <- xyplot(Counts ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
130 data=df, 132 data = df,
131 type='h', 133 type = "h",
132 lwd=1.5, 134 lwd = 1.5,
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)), 135 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)),
134 xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits, 136 xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
135 as.table=T, 137 as.table = T,
136 origin = 0, 138 origin = 0,
137 horizontal=FALSE, 139 horizontal = FALSE,
138 group=Polarity, 140 group = Polarity,
139 col=c("red","blue"), 141 col = c("red", "blue"),
140 par.strip.text = list(cex=0.7), 142 par.strip.text = list(cex = 0.7),
141 ...) 143 ...)
142 p=combineLimits(p) 144 p <- combineLimits(p)
143 } else if (method != "Size") { 145 } else if (method != "Size") {
144 p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), 146 p <- xyplot(eval(as.name(method)) ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
145 data=df, 147 data = df,
146 type= ifelse(method=='Coverage', 'l', 'p'), 148 type = ifelse(method == "Coverage", "l", "p"),
147 pch=19, 149 pch = 19,
148 cex=0.35, 150 cex = 0.35,
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)), 151 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)),
150 xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits, 152 xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
151 as.table=T, 153 as.table = T,
152 origin = 0, 154 origin = 0,
153 horizontal=FALSE, 155 horizontal = FALSE,
154 group=Polarity, 156 group = Polarity,
155 col=c("red","blue"), 157 col = c("red", "blue"),
156 par.strip.text = list(cex=0.7), 158 par.strip.text = list(cex = 0.7),
157 ...) 159 ...)
158 p=combineLimits(p) 160 p <- combineLimits(p)
159 } else { 161 } else {
160 p = barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0, 162 p <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)) + Chromosome, data = df, origin = 0,
161 horizontal=FALSE, 163 horizontal = FALSE,
162 group=Polarity, 164 group = Polarity,
163 stack=TRUE, 165 stack = TRUE,
164 col=c('red', 'blue'), 166 col = c("red", "blue"),
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))), 167 scales = list(y = list(rot = 90, relation = "free", cex = 0.7), x = list(rot = 0, cex = 0.7, axs = "i", tck = c(1, 0))),
166 xlab = NULL, 168 xlab = NULL,
167 ylab = NULL, 169 ylab = NULL,
168 main = NULL, 170 main = NULL,
169 as.table=TRUE, 171 as.table = TRUE,
170 par.strip.text = list(cex=0.6), 172 par.strip.text = list(cex = 0.6),
171 ...) 173 ...)
172 p=combineLimits(p) 174 p <- combineLimits(p)
173 } 175 }
174 return(p) 176 return(p)
175 } 177 }
176 178
177 179
178 ## function parameters 180 ## function parameters
179 181
180 #par.settings.firstplot = list(layout.heights=list(top.padding=11, bottom.padding = -14)) 182 par_settings_firstplot <- list(layout.heights = list(top.padding = -2, bottom.padding = -2), strip.background = list(col = c("lightblue", "lightgreen")))
181 #par.settings.secondplot=list(layout.heights=list(top.padding=11, bottom.padding = -15), strip.background=list(col=c("lavender","deepskyblue"))) 183 par_settings_secondplot <- list(layout.heights = list(top.padding = -1, bottom.padding = -1), strip.background = list(col = c("lightblue", "lightgreen")))
182 par.settings.firstplot = list(layout.heights=list(top.padding=-2, bottom.padding=-2),strip.background=list(col=c("lightblue","lightgreen"))) 184 title_first_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
183 par.settings.secondplot=list(layout.heights=list(top.padding=-1, bottom.padding=-1),strip.background=list(col=c("lightblue","lightgreen"))) 185 title_extra_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
184 title_first_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions") 186 legend_first_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
185 title_extra_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions") 187 legend_extra_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
186 legend_first_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count") 188 bottom_first_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
187 legend_extra_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count") 189 bottom_extra_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
188 bottom_first_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
189 bottom_extra_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
190 190
191 ## Plotting Functions 191 ## Plotting Functions
192 192
193 double_plot <- function(...) { 193 double_plot <- function(...) {
194 page_height = 15 194 page_height <- 15
195 rows_per_page = 10 195 rows_per_page <- 10
196 graph_heights=c(40,30,40,30,40,30,40,30,40,30,10) 196 graph_heights <- c(40, 30, 40, 30, 40, 30, 40, 30, 40, 30, 10)
197 page_width=8.2677 * n_samples / 2 197 page_width <- 8.2677 * n_samples / 2
198 pdf(file=args$output_pdf, paper="special", height=page_height, width=page_width) 198 pdf(file = args$output_pdf, paper = "special", height = page_height, width = page_width)
199 for (i in seq(1,n_genes,rows_per_page/2)) { 199 for (i in seq(1, n_genes, rows_per_page / 2)) {
200 start=i 200 start <- i
201 end=i+rows_per_page/2-1 201 end <- i + rows_per_page / 2 - 1
202 if (end>n_genes) {end=n_genes} 202 if (end > n_genes) {
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)))} 203 end <- n_genes
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))))) 204 }
205 second_plot.list = lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method=args$extra_plot_method, par.settings=par.settings.firstplot), strip.left=strip.custom(par.strip.text = list(cex=0.5)), strip=FALSE))) 205 if (end - start + 1 < 5) {
206 plot.list=rbind(first_plot.list, second_plot.list) 206 graph_heights <- c(rep(c(40, 30), end - start + 1), 10, rep(c(40, 30), 5 - (end - start + 1)))
207 args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 11)), 207 }
208 top=textGrob(paste(title_first_method[[args$first_plot_method]], "and", title_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), vjust=0, just="top"), 208 first_plot_list <- lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings = par_settings_secondplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)))))
209 left=textGrob(paste(legend_first_method[[args$first_plot_method]], "/", legend_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), vjust=0, hjust=0, x=1, y=(-0.38/4)*(end-start-(3.28/0.38)), rot=90), 209 second_plot_list <- lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method = args$extra_plot_method, par.settings = par_settings_firstplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)), strip = FALSE)))
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) 210 plot.list <- rbind(first_plot_list, second_plot_list)
211 args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 11)),
212 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"),
213 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),
214 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)
211 ) 215 )
212 ) 216 )
213 do.call(grid.arrange, args_list) 217 do.call(grid.arrange, args_list)
214 } 218 }
215 devname=dev.off() 219 devname <- dev.off()
216 } 220 }
217 221
218 222
219 single_plot <- function(...) { 223 single_plot <- function(...) {
220 width = 8.2677 * n_samples / 2 224 width <- 8.2677 * n_samples / 2
221 rows_per_page=8 225 rows_per_page <- 8
222 graph_heights=c(rep(40,8),10) 226 graph_heights <- c(rep(40, 8), 10)
223 pdf(file=args$output_pdf, paper="special", height=15, width=width) 227 pdf(file = args$output_pdf, paper = "special", height = 15, width = width)
224 for (i in seq(1,n_genes,rows_per_page)) { 228 for (i in seq(1, n_genes, rows_per_page)) {
225 start=i 229 start <- i
226 end=i+rows_per_page-1 230 end <- i + rows_per_page - 1
227 if (end>n_genes) {end=n_genes} 231 if (end > n_genes) {
228 if (end-start+1 < 8) {graph_heights=c(rep(c(40),end-start+1),10,rep(c(40),8-(end-start+1)))} 232 end <- n_genes
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))))) 233 }
230 plot.list=rbind(first_plot.list) 234 if (end - start + 1 < 8) {
231 args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 9)), 235 graph_heights <- c(rep(c(40), end - start + 1), 10, rep(c(40), 8 - (end - start + 1)))
232 top=textGrob(title_first_method[[args$first_plot_method]], gp=gpar(cex=1), vjust=0, just="top"), 236 }
233 left=textGrob(legend_first_method[[args$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), 237 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)))))
234 sub=textGrob(bottom_first_method[[args$first_plot_method]], gp=gpar(cex=1), just="bottom", vjust=2) 238 plot.list <- rbind(first_plot_list)
239 args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 9)),
240 top = textGrob(title_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, just = "top"),
241 left = textGrob(legend_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, hjust = 0, x = 1, y = (-0.41 / 7) * (end - start - (6.23 / 0.41)), rot = 90),
242 sub = textGrob(bottom_first_method[[args$first_plot_method]], gp = gpar(cex = 1), just = "bottom", vjust = 2)
235 ) 243 )
236 ) 244 )
237 do.call(grid.arrange, args_list) 245 do.call(grid.arrange, args_list)
238 } 246 }
239 devname=dev.off() 247 devname <- dev.off()
240 } 248 }
241 249
242 # main 250 # main
243 251
244 if (args$extra_plot_method != '') { double_plot() } 252 if (args$extra_plot_method != "") {
245 if (args$extra_plot_method == '' & !exists('global', where=args)) { 253 double_plot()
254 }
255 if (args$extra_plot_method == "" & !exists("global", where = args)) {
246 single_plot() 256 single_plot()
247 } 257 }
248 if (exists('global', where=args)) { 258 if (exists("global", where = args)) {
249 pdf(file=args$output, paper="special", height=11.69) 259 pdf(file = args$output, paper = "special", height = 11.69)
250 Table <- within(Table, Counts[Polarity=="R"] <- abs(Counts[Polarity=="R"])) # retropedalage 260 table <- within(table, Counts[Polarity == "R"] <- abs(Counts[Polarity == "R"]))
251 library(reshape2) 261 library(reshape2)
252 ml = melt(Table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size")) 262 ml <- melt(table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
253 if (args$global == "nomerge") { 263 if (args$global == "nomerge") {
254 castml = dcast(ml, Dataset+Polarity+Size ~ variable, function(x) sum(x)) 264 castml <- dcast(ml, Dataset + Polarity + Size ~ variable, function(x) sum(x))
255 castml <- within(castml, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1)) 265 castml <- within(castml, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1))
256 bc = globalbc(castml, global="no") 266 bc <- globalbc(castml, global = "no")
257 } else { 267 } else {
258 castml = dcast(ml, Dataset+Size ~ variable, function(x) sum(x)) 268 castml <- dcast(ml, Dataset + Size ~ variable, function(x) sum(x))
259 bc = globalbc(castml, global="yes") 269 bc <- globalbc(castml, global = "yes")
260 } 270 }
261 plot(bc) 271 plot(bc)
262 devname=dev.off() 272 devname <- dev.off()
263 } 273 }
264