#' Percentage of P-sites per transcript region.
#'
#' This function computes the percentage of P-sites falling in the three
#' annotated regions of the transcripts (5' UTR, CDS and 3' UTR) and generates a
#' bar plot of the resulting values. Multiple samples and replicates can be
#' handled.
#'
#' @param data Either list of data tables or GRangesList object from
#' \code{\link{psite_info}}.
#' @param annotation Data table as generated by \code{\link{create_annotation}}.
#' @param sample Either character string, character string vector or named list
#' of character string(s)/character string vector(s) specifying the name of
#' the sample(s) and replicate(s) of interest. If a list is provided, each
#' element of the list is considered as an independent sample associated with
#' one ore multiple replicates. Multiple samples and replicates are handled
#' and visualised according to \code{multisamples} and \code{plot_style}.
#' @param multisamples Either "average" or "independent". It specifies how to
#' handle multiple samples and replicates stored in \code{sample}:
#' * if \code{sample} is a character string vector and \code{multisamples} is
#' set to "average" the elements of the vector are considered as replicates
#' of one sample and a single bar plot is returned.
#' * if \code{sample} is a character string vector and \code{multisamples} is
#' set to "independent", each element of the vector is analysed independently
#' of the others.
#' * if \code{sample} is a list, \code{multisamples} must be set to "average".
#' Each element of the list is analysed independently of the others, its
#' replicates averaged and its name reported in the plot.
#' Note: when this parameter is set to "average" the bar plot associated with
#' each sample displays the region-specific mean signal computed across the
#' replicates and, if \code{plot_style} is set to "dodge", the corresponding
#' standard error is also reported. Default is "average".
#' @param plot_style Either "stack" or "dodge". It specifies how to organize the
#' bars associated with the three regions of the transcript:
#' * "stack": bars are placed one on top of the other.
#' * "dodge": bars are placed one next to the other. In this case, the
#' standard error obtained by merging multiple samples (if any, see
#' \code{sample} and \code{multisamples}) is displayed. Default is "stack".
#' @param transcripts Character string vector listing the name of transcripts to
#' be included in the analysis. Default is NULL, i.e. all transcripts are
#' used. Please note: transcripts without annotated 5' UTR, CDS and 3' UTR are
#' automatically discarded.
#' @param length_range Integer or integer vector for restricting the analysis to
#' a chosen range of read lengths. Default is NULL, i.e. all read lengths are
#' used. If specified, this parameter prevails over \code{cl}.
#' @param cl Integer value in [1,100] specifying a confidence level for
#' restricting the plot to an automatically-defined range of read lengths. The
#' new range is computed according to the most frequent read lengths, which
#' accounts for the cl% of the sample and is defined by discarding the
#' (100-cl)% of read lengths falling in the tails of the read lengths
#' distribution. If multiple samples are analysed, a single range of read
#' lengths is computed such that at least the cl% of all samples is
#' represented. Default is 100.
#' @param colour Character string vector of three elements specifying the colour
#' of the bar associated with the 5' UTR, CDS and 3' UTR, respectively.
#' Default is a grayscale.
#' @details In the plot, "RNAs" reflects the expected read distribution from
#' random fragmentation of all transcripts used in the analysis. It can be
#' used as baseline to asses the enrichment of ribosomes (P-sites) mapping on
#' the CDS with respect to the UTRs. The three bars are based on the
#' cumulative nucleotide length of the 5' UTRs, CDSs and 3' UTRs,
#' respectively, expressed as percentages.
#' @return List containing: one ggplot object(s) and the data table with the
#' corresponding x-, y-axis values and the z-values, defining the color of the
#' bars ("plot_dt"); an additional data table with raw and scaled number of
#' P-sites per frame for each sample ("count_dt").
#' @examples
#' ## data(reads_list)
#' ## data(mm81cdna)
#' ##
#' ## ## Generate fake samples and replicates
#' ## for(i in 2:6){
#' ## samp_name <- paste0("Samp", i)
#' ## set.seed(i)
#' ## reads_list[[samp_name]] <- reads_list[["Samp1"]][sample(.N, 5000)]
#' ## }
#' ##
#' ## ## Compute and add p-site details
#' ## psite_offset <- psite(reads_list, flanking = 6, extremity = "auto")
#' ## reads_psite_list <- psite_info(reads_list, psite_offset)
#' ##
#' ## ## Define the list of samples and replicate to use as input
#' ## input_samples <- list("S1" = c("Samp1", "Samp2"),
#' ## "S2" = c("Samp3", "Samp4", "Samp5"),
#' ## "S3" = c("Samp6"))
#' ##
#' ## Generate bar plot
#' ## example_psite_per_region <- region_psite(reads_psite_list, mm81cdna,
#' ## sample = input_samples,
#' ## multisamples = "average",
#' ## plot_style = "stack",
#' ## cl = 85,
#' ## colour = c("#333f50", "gray70", "#39827c"))
#' @import data.table
#' @import ggplot2
#' @export
region_psite <- function(data, annotation, sample, multisamples = "average",
plot_style = "stack", transcripts = NULL,
length_range = NULL, cl = 100,
colour = c("gray70", "gray40", "gray10")) {
if(class(data[[1]])[1] == "GRanges"){
data_tmp <- list()
for(i in names(data)){
data_tmp[[i]] <- as.data.table(data[[i]])[, c("width", "strand") := NULL
][, seqnames := as.character(seqnames)]
setnames(data_tmp[[i]], c("seqnames", "start", "end"), c("transcript", "end5", "end3"))
}
data <- data_tmp
}
check_sample <- setdiff(unlist(sample), names(data))
if(length(check_sample) != 0){
cat("\n")
stop(sprintf("incorrect sample name(s): \"%s\" not found\n\n",
paste(check_sample, collapse = ", ")))
}
if(length(sample) == 0){
cat("\n")
stop("at least one sample name must be spcified\n\n")
}
if(!(multisamples %in% c("average", "independent"))){
cat("\n")
warning("parameter multisamples must be either \"average\" or \"independent\".
Set to default \"average\"\n", call. = FALSE)
multisamples <- "average"
}
if(multisamples == "independent" & is.list(sample)) {
cat("\n")
warning("parameter multisamples is set to \"independent\" but parameter sample is a list:
parameter multisamples will be coerced to default \"average\"\n", call. = FALSE)
multisamples <- "average"
}
if(is.character(sample) & length(sample) == 1) {
multisamples <- "independent"
}
if(is.character(sample) & length(sample) > 1 & multisamples == "average") {
sample <- list("Average" = sample)
cat("\n")
warning("Default name of averaged samples is \"Average\":
consider to use a named list of one element to provide a meaningful plot title\n", call. = FALSE)
}
if(!(plot_style %in% c("stack", "dodge"))){
cat("\n")
warning("parameter plot_style must be either \"stack\" or \"dodge\".
Set to default \"stack\"\n", call. = FALSE)
plot_style <- "stack"
}
if(length(length_range) != 0 & !inherits(length_range, "numeric") & !inherits(length_range, "integer")){
cat("\n")
warning("class of length_range is neither numeric nor integer. Set to default NULL\n", call. = FALSE)
length_range = NULL
}
# select transcripts
l_transcripts <- as.character(annotation[l_utr5 > 0 & l_cds > 0 &
l_cds %% 3 == 0 & l_utr3 > 0,
transcript])
if (length(transcripts) == 0) {
c_transcripts <- l_transcripts
} else {
c_transcripts <- intersect(l_transcripts, transcripts)
}
#define length range taking into account all (unlisted) samples
if(length(length_range) == 0){
for(samp in as.character(unlist(sample))){
dt <- data[[samp]][transcript %in% c_transcripts]
if(length(length_range) == 0){
length_range <- seq(quantile(dt$length, (1 - cl/100)/2),
quantile(dt$length, 1 - (1 - cl/100)/2))
} else {
xmin <- min(min(length_range), quantile(dt$length, (1 - cl/100)/2))
xmax <- max(max(length_range), quantile(dt$length, 1 - (1 - cl/100)/2))
length_range <- seq(xmin, xmax)
}
}
}
xmin = min(length_range)
xmax = max(length_range)
# check if all samples have reads of the specified lengths
# especially required if only one read length is passed
if(length(length_range) != 0){
if(is.list(sample)){
samp_dt <- data.table(stack(sample))
setnames(samp_dt, c("sample", "sample_l"))
} else {
samp_dt <- data.table("sample" = sample, "sample_l" = sample)
}
for(samp in samp_dt$sample){
dt <- data[[samp]][transcript %in% c_transcripts]
len_check <- unique(dt$length)
if(sum(length_range %in% len_check) == 0) {
cat("\n")
warning(sprintf("\"%s\" doesn't contain any reads of the selected lengths: sample removed\n", samp), call. = FALSE)
#select element of sample which include the sample to be removed (useful if sample is a list)
sel_l_samp <- samp_dt[sample == samp, sample_l]
#remove the sample from the list/vector
if(is.list(samp)){
sample[[sel_l_samp]] <- sample[[sel_l_samp]][sample[[sel_l_samp]] != samp]
} else {
sample <- sample[sample != samp]
}
}
}
}
if(is.null(unlist(sample))){
cat("\n")
stop("none of the data tables listed in sample contains any reads of the specified lengths\n\n")
}
final_dt <- data.table()
for(samp in as.character(unlist(sample))) {
region_dt <- data[[samp]][as.character(transcript) %in% c_transcripts
][, list(count = .N), by = .(region = psite_region)
][, scaled_count := (count / sum(count)) * 100
][, tmp_samp := samp]
final_dt <- rbind(final_dt, region_dt)
}
final_dt[, class := "mapped"]
# compute length of RNAs for the reference bar
sub_anno <- annotation[as.character(transcript) %in% c_transcripts]
RNA_reg <- colSums(sub_anno[, list(l_utr5, l_cds, l_utr3)])
RNA_reg_perc <- (RNA_reg / sum(RNA_reg)) * 100
RNA_table <- data.table(region = c("5utr", "cds", "3utr"),
count = RNA_reg,
scaled_count = RNA_reg_perc,
tmp_samp = rep("RNAs", 3),
class = "rna")
final_dt <- rbind(final_dt, RNA_table)
final_dt[, region := factor(region,
levels = c("5utr", "cds", "3utr"),
labels = c("5' UTR", "CDS", "3' UTR"))]
final_dt <- final_dt[order(match(tmp_samp, c(as.character(unlist(sample)), "RNAs")), region)]
output <- list()
output[["count_dt"]] <- copy(final_dt[, c("tmp_samp", "region", "count", "scaled_count")])
setnames(output[["count_dt"]], "tmp_samp", "sample")
# compute mean and se of samples if a list is provided
if(is.list(sample)){
samp_dt <- data.table(stack(sample))
samp_dt <- rbind(samp_dt, data.table(values = "RNAs", ind = "RNAs"))
setnames(samp_dt, c("tmp_samp", "sample"))
# set names of samples as specified in parameter sample
final_dt <- merge.data.table(final_dt, samp_dt, sort = FALSE)[, tmp_samp := NULL]
# compute mean and se
plot_dt <- final_dt[, .(mean_scaled_count = mean(scaled_count),
se_scaled_count = sd(scaled_count/sqrt(.N))), by = .(region, sample, class)]
if(identical(plot_style, "stack") | all(lengths(sample) == 1)){
output[["plot_dt"]] <- copy(plot_dt[, c("sample", "mean_scaled_count", "region")])
setnames(output[["plot_dt"]], c("sample", "mean_scaled_count", "region"), c("x", "y", "z"))
} else {
output[["plot_dt"]] <- copy(plot_dt[, c("sample", "mean_scaled_count", "se_scaled_count", "region")])
setnames(output[["plot_dt"]], c("sample", "mean_scaled_count", "se_scaled_count", "region"), c("x", "y", "y_se", "z"))
}
} else {
plot_dt <- final_dt[, sample := tmp_samp
][, se_scaled_count := NA]
setnames(plot_dt, "scaled_count", "mean_scaled_count")
output[["plot_dt"]] <- copy(plot_dt[, c("sample", "mean_scaled_count", "region")])
setnames(output[["plot_dt"]], c("sample", "mean_scaled_count", "region"), c("x", "y", "z"))
}
plot_dt[, sample := factor(sample, levels = unique(sample))]
oldw <- getOption("warn")
options(warn=-1)
bs <- 25
plot <- ggplot(plot_dt, aes(x = sample, y = mean_scaled_count, fill = region))
if(identical(plot_style, "stack")){
plot <- plot + geom_bar(stat = "identity", color = "white", width = 0.65,
size = 0.025 * bs, position = position_stack(reverse = TRUE))
} else {
plot <- plot +
geom_bar(stat = "identity", color = "white",
size = 0.025 * bs, position = position_dodge(0.9)) +
geom_errorbar(aes(ymin = mean_scaled_count - se_scaled_count,
ymax = mean_scaled_count + se_scaled_count,
color = region),
width = 0.35, linewidth = 1.1, na.rm = T,
position = position_dodge(0.9), show.legend = F) +
scale_color_manual(values = colour)
}
plot <- plot +
scale_fill_manual(values = colour) +
theme_bw(base_size = bs) +
scale_x_discrete(breaks = unique(plot_dt$sample)) +
facet_grid( . ~ class, scales = "free", space="free_x") +
scale_y_continuous("% P-sites", sec.axis = sec_axis(~ . * 1 , name = "% nucleotides")) +
theme(axis.title.x = element_blank(), legend.title = element_blank(),
panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(),
strip.background = element_blank(), strip.text = element_blank(),
legend.position = "top", legend.margin = margin(0,0,-5,0),
legend.box.margin = margin(5,0,-10,0),
legend.text = element_text(margin = margin(l = -10, unit = "pt")))
plot
output[["plot"]] <- plot
options(warn = oldw)
return(output)
}
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