Mercurial > repos > artbio > small_rna_map
changeset 5:d65045e976e6 draft
planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_map commit b673d39fbe79f5164ba6489b33cfa78ac238ee09
| author | artbio |
|---|---|
| date | Sat, 22 Jul 2017 11:45:52 -0400 |
| parents | 6ff925458e05 |
| children | f924a33e1eef |
| files | lattice_small_rna_map.r small_rna_map.py small_rna_map.r small_rna_map.xml test-data/coverage.pdf test-data/doubled.tab test-data/doubled_coverage.pdf test-data/doubled_mean.pdf test-data/doubled_median.pdf test-data/input1.bam test-data/input2.bam test-data/mean.pdf test-data/median.pdf test-data/output.pdf test-data/output.tab test-data/output_doubled.pdf test-data/output_doubled.tab test-data/output_lattice.pdf test-data/output_lattice_doubled.pdf test-data/sizes.pdf test-data/sizes.tab |
| diffstat | 21 files changed, 18716 insertions(+), 1797 deletions(-) [+] |
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--- a/lattice_small_rna_map.r Tue Jul 18 17:35:52 2017 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,103 +0,0 @@ -## Setup R error handling to go to stderr -#options( show.error.messages=F, -# error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) -warnings() -library(RColorBrewer) -library(lattice) -library(latticeExtra) -library(grid) -library(gridExtra) -library(optparse) - -option_list <- list( - make_option(c("-r", "--output_tab"), type="character", help="path to tabular file"), - make_option("--output_pdf", type = "character", help="path to the pdf file with plot") - ) - -parser <- OptionParser(usage = "%prog [options] file", option_list = option_list) -args = parse_args(parser) - -# dataset manipulation - -Table = read.delim(args$output_tab, header=T, row.names=NULL) -Table <- within(Table, Nbr_reads[Polarity=="R"] <- (Nbr_reads[Polarity=="R"]*-1)) -n_samples=length(unique(Table$Dataset)) -genes=unique(levels(Table$Chromosome)) -per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x)) -per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) ) -n_genes=length(per_gene_readmap) -## end of data frames implementation - -## functions - -plot_readmap=function(df, ...) { - combineLimits(xyplot(Nbr_reads~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), - data=df, - type='h', - lwd=1.5, - 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)), - xlab=NULL, main=NULL, ylab=NULL, - as.table=T, - origin = 0, - horizontal=FALSE, - group=Polarity, - col=c("red","blue"), - par.strip.text = list(cex=0.7), - ...)) - } - - -plot_size=function(df, ...) { - smR.prepanel=function(x,y,...) {; yscale=c(y*0, max(abs(y)));list(ylim=yscale);} - sizeplot = xyplot(Median~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), - data=df, - type='p', - cex=0.35, - pch=19, - scales= list(relation="free", x=list(rot=0, cex=0, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)), - xlab=NULL, main=NULL, ylab=NULL, - as.table=T, - origin = 0, - horizontal=FALSE, - group=Polarity, - col=c("darkred","darkblue"), - par.strip.text = list(cex=0.7), - ...) - combineLimits(sizeplot) - } - - -## end of functions - -## function parameters -par.settings.readmap=list(layout.heights=list(top.padding=0, bottom.padding=0), strip.background = list(col=c("lightblue","lightgreen")) ) -par.settings.size=list(layout.heights=list(top.padding=0, bottom.padding=0)) -## end of function parameters' - -## GRAPHS - -if (n_genes > 5) {page_height_simple = 11.69; page_height_combi=11.69; rows_per_page=6} else { - rows_per_page= n_genes; page_height_simple = 2.5*n_genes; page_height_combi=page_height_simple*2 } -if (n_samples > 4) {page_width = 8.2677*n_samples/4} else {page_width = 8.2677*n_samples/2} # to test - - -pdf(file=args$output_pdf, paper="special", height=page_height_simple, width=page_width) -if (rows_per_page %% 2 != 0) { rows_per_page = rows_per_page + 1} -for (i in seq(1,n_genes,rows_per_page/2)) { - start=i - end=i+rows_per_page/2-1 - if (end>n_genes) {end=n_genes} - readmap_plot.list=lapply(per_gene_readmap[start:end], function(x) plot_readmap(x, xlim=c(1, x$Chrom_length[1]), strip=FALSE, par.settings=par.settings.readmap)) - size_plot.list=lapply(per_gene_readmap[start:end], function(x) plot_size(x, xlim=c(1, x$Chrom_length[1]), par.settings=par.settings.size)) - - plot.list=rbind(size_plot.list, readmap_plot.list) - args_list=c(plot.list, list(nrow=rows_per_page+1, ncol=1, - top=textGrob("Read Maps and Median sizes", gp=gpar(cex=1), just="top"), - left=textGrob("Read counts / Median size", gp=gpar(cex=1), vjust=1, rot=90), - sub=textGrob("Nucleotide coordinates", gp=gpar(cex=1), just="bottom") - ) - ) -do.call(grid.arrange, args_list) -} -devname=dev.off() -
--- a/small_rna_map.py Tue Jul 18 17:35:52 2017 -0400 +++ b/small_rna_map.py Sat Jul 22 11:45:52 2017 -0400 @@ -12,63 +12,173 @@ nargs='+', help='input BAM files') the_parser.add_argument('--sample_name', dest='sample_name', required=True, nargs='+', help='sample name') - the_parser.add_argument('--output', action="store", + the_parser.add_argument('--output', action='store', type=str, help='output tabular file') + the_parser.add_argument('-S', '--sizes', action='store', + help='use to output read sizes dataframe') args = the_parser.parse_args() return args -def compute_map_dictionary(pysam_object): - ''' - returns - - map_dictionary {(read_chromosome,read_position,polarity): - [list_of_read_length]} - - max_dictionary {chromosome_name: - max_of_number_of_read_at_any_position} - ''' - unmatched_chromosomes = list(pysam_object.references) - map_dictionary = defaultdict(list) - max_dictionary = dict.fromkeys(pysam_object.references, 0) - for read in pysam_object: - if not read.is_unmapped: - read_chromosome = read.reference_name - # to shift from 0-based to 1-based coordinate - read_position = read.pos + 1 - polarity = "F" if (not read.is_reverse) else "R" - map_dictionary[(read_chromosome, read_position, - polarity)].append(read.qlen) - if read_chromosome in unmatched_chromosomes: - unmatched_chromosomes.remove(read_chromosome) - for name in unmatched_chromosomes: - # to put at least a null value by chromosome - map_dictionary[(name, 1, 'F')] = [0] - # make a dictionary with max value of reads by chromosome - max_dictionary = defaultdict(int) - for name in pysam_object.references: - for key in map_dictionary.keys(): - if name in key and len(map_dictionary[key]) > max_dictionary[name]: - max_dictionary[name] = len(map_dictionary[key]) - return map_dictionary, max_dictionary +class Map: + + def __init__(self, bam_file, sample): + self.sample_name = sample + self.bam_object = pysam.AlignmentFile(bam_file, 'rb') + self.chromosomes = dict(zip(self.bam_object.references, + self.bam_object.lengths)) + self.map_dict = self.create_map(self.bam_object) + self.max = self.compute_max(self.map_dict) + self.mean = self.compute_mean(self.map_dict) + self.median = self.compute_median(self.map_dict) + self.coverage = self.compute_coverage(self.map_dict) + self.size = self.compute_size(self.map_dict) + + def create_map(self, bam_object): + ''' + Returns a map_dictionary {(chromosome,read_position,polarity): + [read_length, ...]} + ''' + map_dictionary = dict() + for chrom in self.chromosomes: + for pos in range(self.chromosomes[chrom]): + map_dictionary[(chrom, pos+1, 'F')] = [] # get all chromosomes + map_dictionary[(chrom, pos+1, 'R')] = [] # get all chromosomes + for chrom in self.chromosomes: + for read in bam_object.fetch(chrom): + read_positions = read.positions # a list of covered positions + if read.is_reverse: + map_dictionary[(chrom, read_positions[-1]+1, + 'R')].append(read.query_alignment_length) + else: + map_dictionary[(chrom, read_positions[0]+1, + 'F')].append(read.query_alignment_length) + return map_dictionary + + def compute_max(self, map_dictionary): + ''' + takes a map_dictionary as input and returns + a max_dictionary {(chromosome,read_position,polarity): + max_of_number_of_read_at_any_position} + ''' + merge_keylist = [(i[0], 0) for i in map_dictionary.keys()] + max_dictionary = dict(merge_keylist) + for key in map_dictionary: + if len(map_dictionary[key]) > max_dictionary[key[0]]: + max_dictionary[key[0]] = len(map_dictionary[key]) + return max_dictionary + + def compute_mean(self, map_dictionary): + ''' + takes a map_dictionary as input and returns + a mean_dictionary {(chromosome,read_position,polarity): + mean_value_of_reads} + ''' + mean_dictionary = dict() + for key in map_dictionary: + if len(map_dictionary[key]) == 0: + mean_dictionary[key] = 0 + else: + mean_dictionary[key] = round(numpy.mean(map_dictionary[key]), + 1) + return mean_dictionary + + def compute_median(self, map_dictionary): + ''' + takes a map_dictionary as input and returns + a mean_dictionary {(chromosome,read_position,polarity): + mean_value_of_reads} + ''' + median_dictionary = dict() + for key in map_dictionary: + if len(map_dictionary[key]) == 0: + median_dictionary[key] = 0 + else: + median_dictionary[key] = numpy.median(map_dictionary[key]) + return median_dictionary + + def compute_coverage(self, map_dictionary, quality=10): + ''' + Takes a map_dictionary and returns a dictionary of lists + of coverage along the coordinates of pre_mirs (as keys) + ''' + coverage_dictionary = dict() + for chrom in self.chromosomes: + coverage = self.bam_object.count_coverage( + reference=chrom, + start=0, + end=self.chromosomes[chrom], + quality_threshold=quality) + """ Add the 4 coverage values """ + coverage = [sum(x) for x in zip(*coverage)] + for pos, cov in enumerate(coverage): + coverage_dictionary[(chrom, pos+1, "F")] = cov + coverage_dictionary[(chrom, pos+1, "R")] = cov + return coverage_dictionary + + def compute_size(self, map_dictionary): + ''' + Takes a map_dictionary and returns a dictionary of sizes: + {chrom: {size: {polarity: nbre of reads}}} + ''' + size_dictionary = defaultdict(lambda: defaultdict(lambda: defaultdict( int ))) + for key in map_dictionary: + for size in map_dictionary[key]: + try: + size_dictionary[key[0]][key[2]][size] += 1 + except KeyError: + size_dictionary[key[0]][key[2]][size] = 1 + return size_dictionary + + def write_size_table(self, out): + ''' + Dataset, Chromosome, Polarity, Size, Nbr_reads + out is an *open* file handler + ''' + for chrom in sorted(self.size): + sizes = self.size[chrom]['F'].keys() + sizes.extend(self.size[chrom]['R'].keys()) + for polarity in sorted(self.size[chrom]): + for size in range(min(sizes), max(sizes)+1): + try: + line = [self.sample_name, chrom, polarity, size, + self.size[chrom][polarity][size]] + except KeyError: + line = [self.sample_name, chrom, polarity, size, 0] + line = [str(i) for i in line] + out.write('\t'.join(line) + '\n') + + def write_table(self, out): + ''' + Dataset, Chromosome, Chrom_length, Coordinate, Nbr_reads + Polarity, Max, Mean, Median, Coverage + out is an *open* file handler + ''' + for key in sorted(self.map_dict): + line = [self.sample_name, key[0], self.chromosomes[key[0]], + key[1], len(self.map_dict[key]), key[2], self.max[key[0]], + self.mean[key], self.median[key], self.coverage[key]] + line = [str(i) for i in line] + out.write('\t'.join(line) + '\n') -def main(inputs, sample_names, output): - with open(output, 'w') as outfile: - outfile.write('\t'.join(['Dataset', 'Chromosome', 'Chrom_length', - 'Coordinate', 'Nbr_reads', 'Polarity', - 'Max', 'Mean', 'Median\n'])) - chr_lengths = dict(zip(pysam.AlignmentFile(inputs[0], 'rb').references, - pysam.AlignmentFile(inputs[0], 'rb').lengths)) - for file, sample in zip(inputs, sample_names): - with pysam.AlignmentFile(file, 'rb') as bamfile: - map, maximum = compute_map_dictionary(bamfile) - for record in sorted(map): - line = [sample, record[0], - str(chr_lengths[record[0]]), - str(record[1]), str(len(map[record])), - record[2], str(maximum[record[0]]), - str(round(numpy.mean(map[record]), 1)), - str(numpy.median(map[record]))] - outfile.write("\t".join(line) + "\n") +def main(inputs, samples, file_out, size_file_out=''): + F = open(file_out, 'w') + header = ["Dataset", "Chromosome", "Chrom_length", "Coordinate", + "Nbr_reads", "Polarity", "Max", "Mean", "Median", "Coverage"] + F.write('\t'.join(header) + '\n') + if size_file_out: + Fs = open(size_file_out, 'w') + header = ["Dataset", "Chromosome", "Polarity", "Size", "Nbr_reads"] + Fs.write('\t'.join(header) + '\n') + for file, sample in zip(inputs, samples): + mapobj = Map(file, sample) + mapobj.write_table(F) + if size_file_out: + mapobj.write_size_table(Fs) + F.close() + if size_file_out: + Fs.close() if __name__ == "__main__": @@ -77,4 +187,4 @@ if len(set(args.sample_name)) != len(args.sample_name): args.sample_name = [name + '_' + str(i) for i, name in enumerate(args.sample_name)] - main(args.input, args.sample_name, args.output) + main(args.input, args.sample_name, args.output, args.sizes)
--- a/small_rna_map.r Tue Jul 18 17:35:52 2017 -0400 +++ b/small_rna_map.r Sat Jul 22 11:45:52 2017 -0400 @@ -1,102 +1,141 @@ -library(optparse) -library(ggplot2) +## Setup R error handling to go to stderr +#options( show.error.messages=F, +# error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) +warnings() +library(RColorBrewer) +library(lattice) +library(latticeExtra) +library(grid) library(gridExtra) -library(RColorBrewer) -library(gtable) -library(grid) +library(optparse) option_list <- list( make_option(c("-r", "--output_tab"), type="character", help="path to tabular file"), - make_option("--output_pdf", type = "character", help="path to the pdf file with plot") + make_option(c("-s", "--sizes"), type="character", help="path to size dataframe"), + make_option("--output_pdf", type = "character", help="path to the pdf file with plot"), + make_option("--extra_plot", type = "character", help="what additional data should be plotted") ) parser <- OptionParser(usage = "%prog [options] file", option_list = option_list) args = parse_args(parser) +if (length(args$sizes) != 0) { args$extra_plot <- "SizeDistribution"} -theme_set(theme_bw()) #a theme with a white background +# dataset manipulation + Table = read.delim(args$output_tab, header=T, row.names=NULL) Table <- within(Table, Nbr_reads[Polarity=="R"] <- (Nbr_reads[Polarity=="R"]*-1)) -Chr_limits <- unique(data.frame(Dataset=Table$Dataset, Chromosome=Table$Chromosome, - Chrom_length=Table$Chrom_length)) -Chr_limits_inf <- data.frame(Coordinate=Chr_limits$Chrom_length*0, - Nbr_reads=Chr_limits$Chrom_length*0, - Polarity=rep("F", length(Chr_limits$Dataset)), - Max=Chr_limits$Chrom_length*0, - Mean=Chr_limits$Chrom_length*0, - Median=Chr_limits$Chrom_length*0) -Chr_limits_inf <- cbind(Chr_limits, Chr_limits_inf) -Chr_limits_sup <- data.frame(Coordinate=Chr_limits$Chrom_length+1, - Nbr_reads=Chr_limits$Chrom_length*0, - Polarity=rep("F", length(Chr_limits$Dataset)), - Max=Chr_limits$Chrom_length*0, - Mean=Chr_limits$Chrom_length*0, - Median=Chr_limits$Chrom_length*0) -Chr_limits_sup <- cbind(Chr_limits, Chr_limits_sup) -Table <- rbind(Table, Chr_limits_inf, Chr_limits_sup) - -#To assign colors to categorical variables in ggplot2 that have stable mapping -myColors <- brewer.pal(3,"Set1") -names(myColors) <- levels(Table$Polarity) -colScale <- scale_colour_manual(name = "Polarity",values = myColors) - -#Make initial figures +n_samples=length(unique(Table$Dataset)) +genes=unique(levels(Table$Chromosome)) +per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x)) +per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) ) +n_genes=length(per_gene_readmap) +if (args$extra_plot == "SizeDistribution") { + size=read.delim(args$sizes, header=T, row.names=NULL) + size <- within(size, Nbr_reads[Polarity=="R"] <- (Nbr_reads[Polarity=="R"]*-1)) + per_gene_size=lapply(genes, function(x) subset(size, Chromosome==x)) + } + +## end of data frames implementation + +## functions -p <- ggplot(Table, aes(x=Coordinate, y=Nbr_reads, colour=Polarity)) + - colScale+ - geom_segment(aes(y = 0, x = Coordinate, yend = Nbr_reads, xend = Coordinate, color=Polarity)) + -# geom_segment(aes(y = Nbr_reads, x = 0, yend=Nbr_reads, xend=Chrom_length), alpha=0)+ - facet_wrap(Dataset~Chromosome, scales="free", nrow=1, labeller = label_wrap_gen(multi_line = FALSE))+ -# scale_x_continuous(limits = c(rep(0, length(Table$Chromosome)), Chr_lengths$Chrom_length)) + - scale_y_continuous(breaks = function(x) round(pretty(seq(-(max(x) + 1), (max(x) + 1)))))+ # to display only integer values on y axis - geom_hline(yintercept=0, size=0.3)+ - theme(strip.text = element_text(size = 6, lineheight = 0.1), #specify strip size - panel.grid.major = element_line(colour = "#ffffff"),#conceal major grid lines - panel.grid.minor = element_line(colour = "#ffffff"),#conceal minor grid lines - axis.title = element_blank(),# Conceal axis titles - axis.text = element_text(size = 6),#modify the size of tick labels along axes - legend.position = "none") # Hide the repeate caption +plot_readmap=function(df, ...) { + combineLimits(xyplot(Nbr_reads~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), + data=df, + type='h', + lwd=1.5, + scales= list(relation="free", x=list(rot=0, cex=0.7, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)), + xlab=NULL, main=NULL, ylab=NULL, + as.table=T, + origin = 0, + horizontal=FALSE, + group=Polarity, + col=c("red","blue"), + par.strip.text = list(cex=0.7), + ...)) + } + + +plot_size=function(df, ...) { + #smR.prepanel=function(x,y,...) {; yscale=c(y*0, max(abs(y)));list(ylim=yscale);} + sizeplot = xyplot(eval(as.name(args$extra_plot))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)), + data=df, + type='p', + cex=0.35, + pch=19, + scales= list(relation="free", x=list(rot=0, cex=0, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)), + xlab=NULL, main=NULL, ylab=NULL, + as.table=T, + origin = 0, + horizontal=FALSE, + group=Polarity, + col=c("darkred","darkblue"), + par.strip.text = list(cex=0.7), + ...) + combineLimits(sizeplot) + } -# Create legend -mylegend <- legendGrob(c("F", "R", "Median", "Mean"), pch=22, - gp=gpar(col = c("red","blue","black","yellow"), fill = c("red","blue","black","yellow"))) - -# The second plot -cols<- c("Median"="#000000", "Mean"="#fffa00") -p2 <- ggplot(Table, aes(x = Coordinate, group=1)) + - geom_point(aes(y=Median, colour="Median"), alpha=1, size = 1) + - geom_point(aes(y=Mean, colour="Mean"), alpha= 0.5, size = 1.2)+ - scale_colour_manual(name="", values=cols)+ - expand_limits(y = seq(0,max(Table$Median),by=5)) + - facet_wrap(Dataset~Chromosome, scales="free", nrow=1, labeller = label_wrap_gen(multi_line = FALSE))+ -# geom_segment(aes(y = Nbr_reads, x = 0, yend=Nbr_reads, xend=Chrom_length), alpha=0)+ - scale_y_continuous(limits = c(0,max(Table$Median)), position = "left")+ - theme(strip.background = element_blank(), - strip.text.x = element_blank(), - panel.background = element_rect(fill = NA), - panel.grid.major = element_blank(), - panel.grid.minor = element_blank(), - panel.border = element_rect(fill = NA, colour = "grey50"), - axis.text = element_text(size = 6), - axis.title = element_blank(), - legend.position = "none") +plot_size_distribution= function(df, ...) { +# smR.prepanel=function(x,y,...){; yscale=c(-max(abs(y)), max(abs(y)));list(ylim=yscale);} + bc= barchart(Nbr_reads~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0, + horizontal=FALSE, +group=Polarity, +stack=TRUE, + col=c('red', 'blue'), + cex=0.75, + scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.7), x=list(cex=0.7) ), +# prepanel=smR.prepanel, + xlab = NULL, + ylab = NULL, + main = NULL, + as.table=TRUE, + newpage = T, + par.strip.text = list(cex=0.7), + ...) + combineLimits(bc) + } + + +## end of functions + +## function parameters +par.settings.readmap=list(layout.heights=list(top.padding=0, bottom.padding=0), strip.background = list(col=c("lightblue","lightgreen")) ) +par.settings.size=list(layout.heights=list(top.padding=0, bottom.padding=0)) +graph_title=list(Coverage="Read Maps and Coverages", Median="Read Maps and Median sizes", Mean="Read Maps and Mean sizes", SizeDistribution="Read Maps and Size Distributions") +graph_legend=list(Coverage="Read counts / Coverage", Median="Read counts / Median size", Mean="Read counts / Mean size", SizeDistribution="Read counts") +graph_bottom=list(Coverage="Nucleotide coordinates", Median="Nucleotide coordinates", Mean="Nucleotide coordinates", SizeDistribution="Read sizes / Nucleotide coordinates") +## end of function parameters' -# Transforme ggplot graphs on list of graphs -plot.list1 <- by(data = Table, - INDICES = c(Table$Chromosome), - #simplify = TRUE, - FUN = function(x) {p %+% x } - ) - -plot.list2 <- by(data = Table, - INDICES = c(Table$Chromosome), - simplify = TRUE, - FUN = function(x) { - p2 %+% x - }) +## GRAPHS + +if (n_genes > 5) {page_height_simple = 11.69; page_height_combi=11.69; rows_per_page=6} else { + rows_per_page= n_genes; page_height_simple = 2.5*n_genes; page_height_combi=page_height_simple*2 } +if (n_samples > 4) {page_width = 8.2677*n_samples/4} else {page_width = 8.2677*n_samples/2} # to test + -# Plotting in multiple pages with different rows +pdf(file=args$output_pdf, paper="special", height=page_height_simple, width=page_width) +if (rows_per_page %% 2 != 0) { rows_per_page = rows_per_page + 1} +for (i in seq(1,n_genes,rows_per_page/2)) { + start=i + end=i+rows_per_page/2-1 + if (end>n_genes) {end=n_genes} + readmap_plot.list=lapply(per_gene_readmap[start:end], function(x) plot_readmap(x, strip=FALSE, par.settings=par.settings.readmap)) + if (args$extra_plot == "SizeDistribution") { + size_plot.list=lapply(per_gene_size[start:end], function(x) plot_size_distribution(x, par.settings=par.settings.size)) + } + else { + size_plot.list=lapply(per_gene_readmap[start:end], function(x) plot_size(x, par.settings=par.settings.size)) + } + + + plot.list=rbind(size_plot.list, readmap_plot.list) + args_list=c(plot.list, list(nrow=rows_per_page+1, ncol=1, + top=textGrob(graph_title[[args$extra_plot]], gp=gpar(cex=1), just="top"), + left=textGrob(graph_legend[[args$extra_plot]], gp=gpar(cex=1), vjust=1, rot=90), + sub=textGrob(graph_bottom[[args$extra_plot]], gp=gpar(cex=1), just="bottom") + ) + ) +do.call(grid.arrange, args_list) +} +devname=dev.off() -grobs=rbind(plot.list1,plot.list2) -multi.plot<-do.call(marrangeGrob,list(grobs,ncol=1,nrow=8,top=NULL, - bottom="Coordinates(nt)", left="Number of reads / Median & Mean", right= mylegend)) -ggsave(args$output_pdf, device="pdf", plot=multi.plot, height=11.69, width=8.2) \ No newline at end of file
--- a/small_rna_map.xml Tue Jul 18 17:35:52 2017 -0400 +++ b/small_rna_map.xml Sat Jul 22 11:45:52 2017 -0400 @@ -1,4 +1,4 @@ -<tool id="small_rna_map" name="small rna map" version="0.4.0"> +<tool id="small_rna_map" name="small rna map" version="0.5.0"> <description></description> <requirements> <requirement type="package" version="1.11.2=py27_0">numpy</requirement> @@ -11,37 +11,92 @@ <exit_code range="1:" level="fatal" description="Tool exception" /> </stdio> <command detect_errors="exit_code"><![CDATA[ + #for $file in $input + samtools index '$file' && + #end for python '$__tool_directory__'/small_rna_map.py --input #for $file in $input - '$file' + '$file' #end for - --sample_name + --sample_name #for $sample in $input '$sample.name' #end for + #if $extra_plot == 'Sizes': + --sizes '$output_sizes' + #end if --output '$output_tab' && - Rscript '$__tool_directory__'/lattice_small_rna_map.r --output_tab '$output_tab' --output_pdf '$output_pdf' + Rscript '$__tool_directory__'/small_rna_map.r --output_tab '$output_tab' + --output_pdf '$output_pdf' + #if $extra_plot != 'Sizes': + --extra_plot '$extra_plot' + #else + --sizes $output_sizes + #end if ]]></command> <inputs> - <param name="input" type="data" format="bam" label="Select multiple alignments to parse" multiple="True"/> + <param name="input" type="data" format="bam" label="Select multiple alignments to parse" multiple="True"/> + <param name="extra_plot" type="select" label="select the type of extra plot in addition to read map"> + <option value="Coverage">Coverage</option> + <option value="Mean">Mean Sizes</option> + <option value="Median">Median Sizes</option> + <option value="Sizes">Size Distributions</option> + </param> </inputs> <outputs> - <data format="tabular" name="output_tab" label="tabular file" /> + <data format="tabular" name="output_tab" label="Map dataframe" /> + <data format="tabular" name="output_sizes" label="Size dataframe" > + <filter>extra_plot == "Sizes"</filter> + </data> <data format="pdf" name="output_pdf" label="PDF file" /> + </outputs> <tests> <test> <param name="input" value="input1.bam,input2.bam" ftype="bam"/> + <param name="extra_plot" value="Mean" /> <output file="output.tab" name="output_tab" /> - <output file="output_lattice.pdf" name="output_pdf" /> + <output file="mean.pdf" name="output_pdf" /> + </test> + <test> + <param name="input" value="input1.bam,input1.bam" ftype="bam"/> + <param name="extra_plot" value="Mean" /> + <output file="doubled.tab" name="output_tab" /> + <output file="doubled_mean.pdf" name="output_pdf" /> + </test> + <test> + <param name="input" value="input1.bam,input2.bam" ftype="bam"/> + <param name="extra_plot" value="Median" /> + <output file="output.tab" name="output_tab" /> + <output file="median.pdf" name="output_pdf" /> </test> <test> <param name="input" value="input1.bam,input1.bam" ftype="bam"/> - <output file="output_doubled.tab" name="output_tab" /> - <output file="output_lattice_doubled.pdf" name="output_pdf" /> + <param name="extra_plot" value="Median" /> + <output file="doubled.tab" name="output_tab" /> + <output file="doubled_median.pdf" name="output_pdf" /> + </test> + <test> + <param name="input" value="input1.bam,input2.bam" ftype="bam"/> + <param name="extra_plot" value="Coverage" /> + <output file="output.tab" name="output_tab" /> + <output file="coverage.pdf" name="output_pdf" /> + </test> + <test> + <param name="input" value="input1.bam,input1.bam" ftype="bam"/> + <param name="extra_plot" value="Coverage" /> + <output file="doubled.tab" name="output_tab" /> + <output file="doubled_coverage.pdf" name="output_pdf" /> + </test> + <test> + <param name="input" value="input1.bam,input2.bam" ftype="bam"/> + <param name="extra_plot" value="Sizes" /> + <output file="output.tab" name="output_tab" /> + <output file="sizes.tab" name="output_sizes" /> + <output file="sizes.pdf" name="output_pdf" /> </test> </tests> @@ -54,10 +109,15 @@ In addition to the read counts (bars), median size and mean size of reads mapping at a given position are plotted in the graphs. **Inputs** -Bam alignment files. Be carreful to select Bam corresponding to alignments to the same reference + +bam alignment files that *must* be + + - sorted + - mapping to the same reference **Output** -A pdf file generated by the R package ggplot2 + +A pdf file generated by the R package lattice </help>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/doubled.tab Sat Jul 22 11:45:52 2017 -0400 @@ -0,0 +1,8657 @@ +Dataset Chromosome Chrom_length Coordinate Nbr_reads Polarity Max Mean Median Coverage +input1.bam_0 FBtr0070001 72 1 1 F 27 17.0 17.0 1 +input1.bam_0 FBtr0070001 72 1 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 2 0 F 27 0 0 1 +input1.bam_0 FBtr0070001 72 2 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 3 0 F 27 0 0 1 +input1.bam_0 FBtr0070001 72 3 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 4 0 F 27 0 0 1 +input1.bam_0 FBtr0070001 72 4 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 5 0 F 27 0 0 1 +input1.bam_0 FBtr0070001 72 5 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 6 0 F 27 0 0 1 +input1.bam_0 FBtr0070001 72 6 0 R 27 0 0 1 +input1.bam_0 FBtr0070001 72 7 6 F 27 20.3 21.0 7 +input1.bam_0 FBtr0070001 72 7 0 R 27 0 0 7 +input1.bam_0 FBtr0070001 72 8 3 F 27 21.3 21.0 10 +input1.bam_0 FBtr0070001 72 8 0 R 27 0 0 10 +input1.bam_0 FBtr0070001 72 9 0 F 27 0 0 10 +input1.bam_0 FBtr0070001 72 9 0 R 27 0 0 10 +input1.bam_0 FBtr0070001 72 10 0 F 27 0 0 10 +input1.bam_0 FBtr0070001 72 10 0 R 27 0 0 10 +input1.bam_0 FBtr0070001 72 11 2 F 27 20.5 20.5 12 +input1.bam_0 FBtr0070001 72 11 0 R 27 0 0 12 +input1.bam_0 FBtr0070001 72 12 0 F 27 0 0 12 +input1.bam_0 FBtr0070001 72 12 0 R 27 0 0 12 +input1.bam_0 FBtr0070001 72 13 0 F 27 0 0 12 +input1.bam_0 FBtr0070001 72 13 0 R 27 0 0 12 +input1.bam_0 FBtr0070001 72 14 0 F 27 0 0 12 +input1.bam_0 FBtr0070001 72 14 0 R 27 0 0 12 +input1.bam_0 FBtr0070001 72 15 0 F 27 0 0 12 +input1.bam_0 FBtr0070001 72 15 0 R 27 0 0 12 +input1.bam_0 FBtr0070001 72 16 1 F 27 19.0 19.0 13 +input1.bam_0 FBtr0070001 72 16 0 R 27 0 0 13 +input1.bam_0 FBtr0070001 72 17 1 F 27 23.0 23.0 14 +input1.bam_0 FBtr0070001 72 17 0 R 27 0 0 14 +input1.bam_0 FBtr0070001 72 18 1 F 27 16.0 16.0 14 +input1.bam_0 FBtr0070001 72 18 0 R 27 0 0 14 +input1.bam_0 FBtr0070001 72 19 0 F 27 0 0 14 +input1.bam_0 FBtr0070001 72 19 0 R 27 0 0 14 +input1.bam_0 FBtr0070001 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--- a/test-data/output.tab Tue Jul 18 17:35:52 2017 -0400 +++ b/test-data/output.tab Sat Jul 22 11:45:52 2017 -0400 @@ -1,773 +1,8657 @@ -Dataset Chromosome Chrom_length Coordinate Nbr_reads Polarity Max Mean Median -input1.bam FBtr0070911 73 1 1 F 1 23.0 23.0 -input1.bam FBtr0070911 73 4 1 F 1 20.0 20.0 -input1.bam FBtr0070911 73 7 1 F 1 15.0 15.0 -input1.bam FBtr0071581 71 1 2 F 2 21.0 21.0 -input1.bam FBtr0071581 71 4 1 F 2 24.0 24.0 -input1.bam FBtr0071630 71 1 1 F 1 22.0 22.0 -input1.bam FBtr0071630 71 3 1 F 1 25.0 25.0 -input1.bam FBtr0071736 72 3 6 F 6 25.0 26.0 -input1.bam FBtr0072445 72 1 2 F 2 18.0 18.0 -input1.bam FBtr0072445 72 2 1 F 2 19.0 19.0 -input1.bam FBtr0072447 72 1 3 F 3 18.0 18.0 -input1.bam FBtr0072580 74 14 1 F 1 20.0 20.0 -input1.bam FBtr0072580 74 35 1 F 1 19.0 19.0 -input1.bam FBtr0072580 74 36 1 F 1 18.0 18.0 -input1.bam FBtr0072600 72 1 1 F 1 19.0 19.0 -input1.bam FBtr0072600 72 43 1 F 1 28.0 28.0 -input1.bam FBtr0072683 72 1 1 F 1 25.0 25.0 -input1.bam 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FBtr0083499 73 1 27 F 27 21.7 22.0 -input1.bam FBtr0083499 73 3 1 F 27 20.0 20.0 -input1.bam FBtr0083499 73 4 1 F 27 19.0 19.0 -input1.bam FBtr0083501 72 1 1 F 2 16.0 16.0 -input1.bam FBtr0083501 72 4 1 F 2 17.0 17.0 -input1.bam FBtr0083501 72 15 1 F 2 23.0 23.0 -input1.bam FBtr0083501 72 31 2 F 2 23.0 23.0 -input1.bam FBtr0083544 72 1 1 F 7 23.0 23.0 -input1.bam FBtr0083544 72 3 7 F 7 21.3 21.0 -input1.bam FBtr0083544 72 38 1 F 7 16.0 16.0 -input1.bam FBtr0083545 72 3 8 F 8 20.6 21.0 -input1.bam FBtr0083545 72 45 1 F 8 17.0 17.0 -input1.bam FBtr0083693 72 4 1 F 1 27.0 27.0 -input1.bam FBtr0083693 72 32 1 F 1 22.0 22.0 -input1.bam FBtr0083693 72 33 1 F 1 21.0 21.0 -input1.bam FBtr0083793 73 1 30 F 30 22.1 22.0 -input1.bam FBtr0084597 84 9 1 F 1 17.0 17.0 -input1.bam FBtr0084597 84 12 1 F 1 15.0 15.0 -input1.bam FBtr0084597 84 48 1 F 1 18.0 18.0 -input1.bam FBtr0085979 73 58 1 R 1 15.0 15.0 -input1.bam FBtr0086006 73 41 1 F 1 23.0 23.0 -input1.bam FBtr0086006 73 44 1 F 1 20.0 20.0 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