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#' Plot the raw fragment size metrics of single group in a single plot,
#' colored by samples.
#'
#' @importFrom magrittr %>%
#' @import ggplot2
#' @import stringr
#' @import dplyr
#' @param x A long-format dataframe contains the metrics of different cohort.
#' @param order The groups show in the final plot,
#' the input value should be vector,
#' e.g. `order = c('group1')``,
#' default is all groups/cohorts in the folder path.
#' @param plot The plot type, default is 'all' which means both proportion,
#' cdf and 1-cdf plots will be shown.
#' @param vline Vertical dashed lines, default value is `c(81,167)`.
#' @param xlim The x axis range shown in the plot. Default is `c(0,500)`.
#' @param ylim The y axis range shown in the fraction of fragment size plots.
#' Default is `c(0,0.035)`.
#' @param ... Further arguments passed to or from other methods.
#' @return The function returns a list plots.
#' @examples
#' # Get the path to example data.
#' path <- examplePath("groups_picard")
#'
#' # Calculate the metrics.
#' df <- callMetrics(path = path)
#'
#' # Plot the only the group specified..
#' plot <- plotSingleGroup(x = df, order = c("cohort_1"))
#' @author Haichao Wang
#'
#' @export
plotSingleGroup <-
function(x, xlim, ylim, vline, order, plot, ...) {
group <-
insert_size <- prop <- file_name <- cdf <- one_minus_cdf <- NULL
# Setting vline.
if (missing(vline)) {
vline <- c(81, 167)
}
# Setting the plot.
if (missing(plot)) {
plot <- "all"
}
# Setting the orders of groups in the plot.
if (missing(order)) {
order <- as.vector(unique(x$group))
}
# Setting xlim.
if (missing(xlim)) {
xlim <- c(0, 500)
}
# Setting ylim for proportion plots.
if (missing(ylim)) {
ylim <- c(0, 0.03)
}
# Filtering the groups.
x <- filter(x, group %in% as.vector(order))
# Setting the factor level of the groups.
x$group <- factor(x$group, levels = order)
# Prop plot.
prop_plot <-
ggplot(
data = x,
aes(x = insert_size, y = prop, color = file_name)
) +
geom_line(size = 1) +
xlim(xlim) +
ylim(ylim) +
xlab("Fragment size (bp)") +
ylab("Proportion of reads") +
theme_classic() +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 14, face = "bold"),
legend.position = "none"
)
# Cdf plot
cdf_plot <-
ggplot(
data = x,
aes(x = insert_size, y = cdf, color = file_name)
) +
geom_line(size = 1) +
xlim(xlim) +
xlab("Fragment size (bp)") +
ylab("Cumulative proportion of reads") +
theme_classic() +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 14, face = "bold"),
legend.position = "none"
)
# 1-cdf plot
one_minus_cdf_plot <-
ggplot(
data = x,
aes(x = insert_size, y = one_minus_cdf, color = file_name)
) +
geom_line(size = 1) +
xlim(xlim) +
xlab("Fragment size (bp)") +
ylab("Cumulative proportion of reads > fragment size") +
theme_classic() +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 14, face = "bold"),
legend.position = "none"
)
# Add vertical lines to the plot
for (i in c(seq_len(length(vline)))) {
prop_plot <-
prop_plot + geom_vline(
xintercept = vline[i],
linetype = "dashed",
color = "red",
size = 0.6
)
cdf_plot <-
cdf_plot + geom_vline(
xintercept = vline[i],
linetype = "dashed",
color = "red",
size = 0.6
)
one_minus_cdf_plot <-
one_minus_cdf_plot + geom_vline(
xintercept = vline[i],
linetype = "dashed",
color = "red",
size = 0.6
)
}
return(
list(
prop_plot = prop_plot,
cdf_plot = cdf_plot,
one_minus_cdf_plot = one_minus_cdf_plot
)
)
}
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