#' Metaheatmaps of the two extremities of the reads.
#'
#' This function generates four metaheatmaps displaying the abundance of the 5'
#' and 3' extremity of reads mapping around the start and the stop codon of
#' annotated CDSs, stratified by their length. Multiple samples and replicates
#' can be handled.
#'
#' @param data Either list of data tables or GRangesList object from
#' \code{\link{bamtolist}}, \code{\link{bedtolist}},
#' \code{\link{length_filter}} or \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 heatmap 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. The number of plots returned and their organization is
#' specified by \code{plot_style}.
#' * 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. The number of plots
#' returned and their organization is specified by \code{plot_style}.
#' Note: when this parameter is set to "average" the heatmap associated with
#' each sample displays the length- and position- specific mean signal
#' computed across the replicates. Default is "average".
#' @param plot_style Either "split" or "facet". It specifies how to organize and
#' display multiple heatmaps:
#' * "split": one heatmap for each sample is returned as an independent
#' ggplot object.
#' * "facet": the heatmaps are placed one below the other, in independent
#' boxes.
#' Default is "split".
#' @param scale_factors Either "auto", a named numeric vector or "none". It
#' specifies how heatmap values should be scaled before merging multiple
#' replicates (if any):
#' * "auto": each heatmap is scaled so that the average of all values is 1.
#' * named numeric vector: \code{scale_factors} must be the same length of
#' unlisted \code{sample} and each scale factor must be named after the
#' corresponding string in unlisted \code{sample}. No specific order is
#' required. Each heatmap value is multiplied by the matching scale factor.
#' * "none": no scaling is applied.
#' Default is "auto".
#' @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 with either 5' UTR, coding sequence or 3'
#' UTR shorter than \code{utr5l}, \eqn{2*}\code{cdsl} and \code{utr3l},
#' respectively, are automatically discarded.
#' @param length_range Integer or integer vector for restricting the plot 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 utr5l Positive integer specifying the length (in nucleotides) of the
#' 5' UTR region flanking the start codon to be considered in the analysis.
#' Default is 50.
#' @param cdsl Positive integer specifying the length (in nucleotides) of the
#' CDS regions flanking both the start and stop codon to be considered in the
#' analysis. Default is 50.
#' @param utr3l Positive integer specifying the length (in nucleotides) of the
#' 3' UTR region flanking the stop codon to be considered in the analysis.
#' Default is 50.
#' @param colour Character string specifying the colour of the plot. The colour
#' scheme is as follow: tiles corresponding to the lowest signal are always
#' white, tiles corresponding to the highest signal are of the specified
#' colour and the progression between these two colours follows either linear
#' or logarithmic gradients (see \code{log_colour}). Default is "black".
#' @param log_colour Logical value whether to use a logarithmic colour scale
#' (strongly suggested in case of large signal variations). Default is FALSE.
#' @return List containing: one or more ggplot object(s) and the data table with
#' the corresponding x- and y-axis values and the values defining the color of
#' the tiles ("plot_dt"); an additional data table with raw and scaled number
#' of read extremities mapping around the start and the stop codon, per
#' length, 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)]
#' }
#'
#' ## 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 metaheatmaps for a sub-range of read lengths:
#' example_ends_heatmap <- rends_heat(reads_list, mm81cdna,
#' sample = input_samples,
#' multisamples = "average",
#' plot_style = "split",
#' cl = 85,
#' utr5l = 25, cdsl = 40, utr3l = 25)
#' @import data.table
#' @import ggplot2
#' @export
rends_heat <- function(data, annotation, sample, multisamples = "average",
plot_style = "split", scale_factors = "auto",
transcripts = NULL, length_range = NULL, cl = 100,
utr5l = 50, cdsl = 50, utr3l = 50, log_colour = F,
colour = "black") {
if(class(data[[1]])[1] == "GRanges"){
data_tmp <- list()
for(i in unlist(sample)){
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"
plot_style <- "split"
}
if(is.character(sample) & length(sample) > 1 & multisamples == "average") {
sample <- list("Average" = sample)
plot_style <- "split"
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(is.list(sample) & length(sample) == 1){
plot_style <- "split"
}
if(!(plot_style %in% c("split", "facet"))){
cat("\n")
warning("parameter plot_style must be either \"split\" or \"facet\".
Set to default \"split\"\n", call. = FALSE)
plot_style <- "split"
}
# select transcripts
l_transcripts <- as.character(annotation[l_utr5 >= utr5l &
l_cds > 2 * (cdsl + 1) &
l_utr3 >= utr3l, 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)
}
}
}
minlen = min(length_range)
maxlen = 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$sample){
dt <- data[[samp]][cds_start != 0 & cds_stop !=0]
if(length(c_transcripts) != 0) {
dt <- dt[transcript %in% c_transcripts]
}
len_check <- unique(dt$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")
}
# compute signal for heatmaps
final_dt <- data.table()
for(samp in as.character(unlist(sample))){
dt <- data[[samp]][transcript %in% c_transcripts]
# add distances of extremities from start and codons
dt[, start_dist_end5 := end5 - cds_start
][, stop_dist_end5 := end5 - cds_stop
][, start_dist_end3 := end3 - cds_start
][, stop_dist_end3 := end3 - cds_stop]
# 5' end signal from start
start_sub <- dt[start_dist_end5 %in% seq(-utr5l, cdsl)]
start_tab <- setkey(start_sub, length, start_dist_end5
)[CJ(length_range, start_dist_end5 = seq(-utr5l, cdsl)), .N, by=.EACHI
][, region := "start"]
# 5' end signal from stop
stop_sub <- dt[stop_dist_end5 %in% seq(-cdsl, utr3l)]
stop_tab <- setkey(stop_sub, length, stop_dist_end5
)[CJ(length_range, stop_dist_end5 = seq(-cdsl, utr3l)), .N, by=.EACHI
][, region := "stop"]
# merge 5' end tables
final_tab5 <- rbind(start_tab, stop_tab, use.names = FALSE)[, end := "5end"]
# 3' end signal from start
start_sub <- dt[start_dist_end3 %in% seq(-utr5l, cdsl)]
start_tab <- setkey(start_sub, length, start_dist_end3
)[CJ(length_range, start_dist_end3 = seq(-utr5l, cdsl)), .N, by=.EACHI
][, region := "start"]
# 3' end signal from stop
stop_sub <- dt[stop_dist_end3 %in% seq(-cdsl, utr3l)]
stop_tab <- setkey(stop_sub, length, stop_dist_end3
)[CJ(length_range, stop_dist_end3 = seq(-cdsl, utr3l)), .N, by=.EACHI
][, region := "stop"]
# merge 3' end tables
final_tab3 <- rbind(start_tab, stop_tab, use.names = FALSE)[, end := "3end"]
# merge all
samp_final_tab <- rbind(final_tab5, final_tab3, use.names = FALSE)
setnames(samp_final_tab, c("length", "distance", "count", "region", "end"))
# scaling/normalization
if(is.character(scale_factors) & scale_factors[1] == "auto"){
samp_final_tab[, scaled_count := (count / mean(count))]
} else {
if(is.numeric(scale_factors)){
samp_final_tab[, scaled_count := count * scale_factors[samp]]
} else {
samp_final_tab[, scaled_count := count]
}
}
samp_final_tab[, tmp_samp := samp]
final_dt <- rbind(final_dt, samp_final_tab)
}
final_dt[, region := factor(region, levels = c("start", "stop"),
labels = c("Distance from start (nt)", "Distance from stop (nt)"))
][, end := factor(end, levels = c("5end", "3end"), labels = c("5' end", "3' end"))]
output <- list()
output[["count_dt"]] <- copy(final_dt[, c("tmp_samp", "region", "end", "length", "distance", "count", "scaled_count")])
if(is.character(scale_factors) & scale_factors[1] == "none"){
output[["count_dt"]][, scaled_count := NULL]
}
setnames(output[["count_dt"]], "tmp_samp", "sample")
# compute mean samples if a list is provided
if(is.list(sample)){
samp_dt <- data.table(stack(sample))
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
plot_dt <- final_dt[, .(mean_scaled_count = mean(scaled_count)), by = .(length, region, end, distance, sample)]
} else {
plot_dt <- final_dt[, sample := tmp_samp]
setnames(plot_dt, "scaled_count", "mean_scaled_count")
}
output[["plot_dt"]] <- copy(plot_dt[, c("sample", "region", "end", "distance", "length", "mean_scaled_count")])
setnames(output[["plot_dt"]], c("distance", "length", "mean_scaled_count"), c("x", "y", "z"))
plot_dt[, sample := factor(sample, levels = unique(sample))]
zmax <- max(plot_dt$mean_scaled_count)
oldw <- getOption("warn")
options(warn=-1)
if(plot_style == "split"){
for(samp in unique(plot_dt$sample)){ # generate a plot for each sample and store it
sub_plot_dt <- plot_dt[sample == samp]
plot <- ggplot(sub_plot_dt, aes(distance, length)) +
geom_tile(aes(fill = mean_scaled_count)) +
labs(title = samp, y = "Read length") +
theme_bw(base_size = 22) +
facet_grid(end ~ region, scales = "free", switch = "x") +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(),
axis.title.x = element_blank(), plot.title = element_text(hjust = 0.5),
strip.background = element_blank(), strip.placement = "outside") +
scale_y_continuous(limits = c(minlen - 0.5, maxlen + 0.5),
breaks = seq(minlen + ((minlen) %% 2), maxlen, by = max(2,floor((maxlen - minlen)/7)))) +
geom_vline(xintercept = 0, linetype = 2, color = "red")
if(log_colour == F) {
plot <- plot +
scale_fill_gradient("# read\nextremities\n", low = "white", high = colour,
limits = c(0.1, zmax), breaks = c(0.1, zmax/2, zmax),
labels = c("0", floor(zmax/2), floor(zmax)), na.value = "white")
} else {
plot <- plot +
scale_fill_gradient("# read\nextremities\n", low = "white", high = colour,
limits = c(0.1, zmax), breaks = c(0.1, 10^(log10(zmax)/2 - 0.5), floor(zmax)),
labels = c("0", floor(10^(log10(zmax)/2 - 0.5)), floor(zmax)), trans = "log", na.value = "transparent")
}
output[[paste0("plot_", samp)]] <- plot
}
} else {
plot <- ggplot(plot_dt, aes(distance, length)) +
geom_tile(aes(fill = mean_scaled_count)) +
labs(y = "Read length") +
theme_bw(base_size = 22) +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(),
axis.title.x = element_blank()) +
facet_grid(sample + end ~ region, scales = "free", switch = "x") +
theme(strip.background = element_blank(), strip.placement = "outside") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(limits = c(minlen - 0.5, maxlen + 0.5),
breaks = seq(minlen + ((minlen) %% 2),maxlen, by = max(2,floor((maxlen - minlen)/7)))) +
geom_vline(xintercept = 0, linetype = 2, color = "red")
if (log_colour == F) {
plot <- plot +
scale_fill_gradient("# read\nextremities\n", low = "white", high = colour,
limits = c(0.1, zmax), breaks = c(0.1, zmax/2, zmax),
labels = c("0", floor(zmax/2), floor(zmax)), na.value = "white")
} else {
plot <- plot +
scale_fill_gradient("# read\nextremities\n", low = "white", high = colour,
limits = c(0.1, zmax), breaks = c(0.1, 10^(log10(zmax)/2 - 0.5), floor(zmax)),
labels = c("0", floor(10^(log10(zmax)/2 - 0.5)), floor(zmax)), trans = "log", na.value = "transparent")
}
output[["plot"]] <- plot
}
options(warn = oldw)
return(output)
}
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