annotate small_rna_maps.r @ 6:a3be3601bcb3 draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 9f9c64aff0d225881bedb97bd5035ccbca945d9d

## Setup R error handling to go to stderr
options( show.error.messages=F,
       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
# options(warn = -1)
library(RColorBrewer)
library(lattice)
library(latticeExtra)
library(grid)
library(gridExtra)
library(optparse)

option_list <- list(
    make_option(c("-f", "--first_dataframe"), type="character", help="path to first dataframe"),
    make_option(c("-e", "--extra_dataframe"), type="character", help="path to additional dataframe"),
    make_option(c("-n", "--normalization"), type="character", help="space-separated normalization/size factors"),
    make_option("--first_plot_method", type = "character", help="How additional data should be plotted"),
    make_option("--extra_plot_method", type = "character", help="How additional data should be plotted"),
    make_option("--output_pdf", type = "character", help="path to the pdf file with plots")
    )
 
parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
args = parse_args(parser)
 
# data frames implementation
## first table
Table = read.delim(args$first_dataframe, header=T, row.names=NULL)
if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") {
    Table <- within(Table, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
}
n_samples=length(unique(Table$Dataset))
samples = unique(Table$Dataset)
if (args$normalization != "") {
    norm_factors = as.numeric(unlist(strsplit(args$normalization, " ")))
} else {
    norm_factors = rep(1, n_samples)
}
if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") {
    i = 1
    for (sample in samples) {
        print(norm_factors[i])
        Table[, length(Table)][Table$Dataset==sample] <- Table[, length(Table)][Table$Dataset==sample]*norm_factors[i]
        i = i + 1
    }
    print(tail(Table))
}
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)
# second table
if (args$extra_plot_method != '') {
    ExtraTable=read.delim(args$extra_dataframe, header=T, row.names=NULL)
    if (args$extra_plot_method == "Counts" | args$extra_plot_method=='Size') {
        ExtraTable <- within(ExtraTable, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
    }
    if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") {
        i = 1
        for (sample in samples) {
            ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample] <- ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample]*norm_factors[i]
            i = i + 1
        }
    }
    per_gene_size=lapply(genes, function(x) subset(ExtraTable, Chromosome==x))
}

## functions

plot_unit = function(df, method=args$first_plot_method, ...) {
    if (method == 'Counts') {
        p = xyplot(Counts~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),
        ...)
    } else if (method != "Size") {
        p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
        data=df,
        type='p',
        pch=19,
        cex=0.35,
        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),
        ...)
    } else {
        p = barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
                     horizontal=FALSE,
                     group=Polarity,
                     stack=TRUE,
                     col=c('red', 'blue'),
                     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)),
        xlab = NULL,
        ylab = NULL,
        main = NULL,
        as.table=TRUE,
        par.strip.text = list(cex=0.6),
        ...)
    }
    combineLimits(p)
}

plot_single <- function(df, method=args$first_plot_method, rows_per_page=rows_per_page, ...) {
    if (method == 'Counts') {
        p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
                   data=df,
                   type='h',
                   lwd=1.5,
                   scales= list(relation="free", x=list(rot=0, cex=0.7, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)),
                   xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
                   ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
                   main=title_first_method[[args$first_plot_method]],
                   origin = 0,
                   group=Polarity,
                   col=c("red","blue"),
                   par.strip.text = list(cex=0.7),
                   as.table=T,
                   ...)
        p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
        return(p)
    } else if (method != "Size") {
        p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
                   data=df,
                   type='p',
                   pch=19,
                   cex=0.35,
                   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=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
                   ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
                   main=title_first_method[[args$first_plot_method]],
                   origin = 0,
                   group=Polarity,
                   col=c("red","blue"),
                   par.strip.text = list(cex=0.7),
                   as.table=T,
                   ...)
        p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
        return(p)
    } else {
        p= barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
                    horizontal=FALSE,
                    group=Polarity,
                    stack=TRUE,
                    col=c('red', 'blue'),
                    scales=list(y=list(tick.number=4, rot=90, relation="free", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
                    xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
                    ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
                    main=title_first_method[[args$first_plot_method]],
                    par.strip.text = list(cex=0.7),
                    nrow = 8,
                    as.table=TRUE,
                    
                    ...)
          p = update(useOuterStrips(p, strip.left=strip.custom(par.strip.text = list(cex=0.5))), layout=c(n_samples, rows_per_page))
          
          p = combineLimits(p, extend=TRUE)
          return (p)
        }
}

## function parameters

#par.settings.firstplot = list(layout.heights=list(top.padding=11, bottom.padding = -14))
#par.settings.secondplot=list(layout.heights=list(top.padding=11, bottom.padding = -15), strip.background=list(col=c("lavender","deepskyblue")))
par.settings.firstplot = list(layout.heights=list(top.padding=-2, bottom.padding=-2))
par.settings.secondplot=list(layout.heights=list(top.padding=-1, bottom.padding=-1), strip.background=list(col=c("lavender","deepskyblue")))
par.settings.single_plot=list(strip.background = list(col = c("lightblue", "lightgreen")))
title_first_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
title_extra_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
legend_first_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
legend_extra_method =list(Counts="Read count", Coverage="Coveragedepth", Median="Median size", Mean="Mean size", Size="Read count")
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")
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")

## Plotting Functions

double_plot <- function(...) {
    if (n_genes > 5) {page_height=15; rows_per_page=10} else {
                     rows_per_page= 2 * n_genes; page_height=1.5*n_genes}
    if (n_samples > 4) {page_width = 8.2677*n_samples/4} else {page_width = 7 * n_samples/2}
    pdf(file=args$output_pdf, paper="special", height=page_height, width=page_width)
    for (i in seq(1,n_genes,rows_per_page/2)) {
        start=i
        end=i+rows_per_page/2-1
        if (end>n_genes) {end=n_genes}
        first_plot.list = lapply(per_gene_readmap[start:end], function(x) plot_unit(x, strip=FALSE, par.settings=par.settings.firstplot))
        second_plot.list = lapply(per_gene_size[start:end], function(x) plot_unit(x, method=args$extra_plot_method, par.settings=par.settings.secondplot))
        plot.list=rbind(second_plot.list, first_plot.list)
        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)),
                                    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"),
                                    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),
                                    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)
                                    )
                   )
        do.call(grid.arrange, args_list)
        }
    devname=dev.off()
}


single_plot <- function(...) {
    width = 8.2677 * n_samples / 2
    rows_per_page=8
    pdf(file=args$output_pdf, paper="special", height=11.69, width=width)
    for (i in seq(1,n_genes,rows_per_page)) {
        start=i
        end=i+rows_per_page-1
        if (end>n_genes) {end=n_genes}
        bunch = do.call(rbind, per_gene_readmap[start:end]) # sub dataframe from the list
        p = plot_single(bunch, method=args$first_plot_method, par.settings=par.settings.single_plot, rows_per_page=rows_per_page)
        plot(p)
        }
    devname=dev.off()
}

# main

if (args$extra_plot_method != '') { double_plot() }
if (args$extra_plot_method == '') {
    single_plot()
}