#' Plot 192 trinucleotide profile
#'
#' Plot relative contribution of 192 trinucleotides
#' @param mut_matrix 192 trinucleotide profile matrix
#' @param ymax Y axis maximum value, default = 0.2
#' @param colors 6 value color vector
#' @param condensed More condensed plotting format. Default = F.
#' @return 192 trinucleotide profile plot
#'
#' @import ggplot2
#' @importFrom magrittr %>%
#'
#' @examples
#' ## See the 'mut_matrix_stranded()' example for how we obtained the
#' ## mutation matrix with transcriptional strand information:
#' mut_mat_s <- readRDS(system.file("states/mut_mat_s_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Plot profile for some of the samples
#' plot_192_profile(mut_mat_s[, c(1, 4, 7)])
#'
#' ## You can create a more condensed version of the plot
#' plot_192_profile(mut_mat_s[, c(1, 4, 7)], condensed = TRUE)
#'
#' ## It's also possible to plot signatures, for example signatures
#' ## generated with NMF
#' ## See 'extract_signatures()' on how we obtained these signatures.
#' nmf_res_strand <- readRDS(system.file("states/nmf_res_strand_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Optionally, provide signature names
#' colnames(nmf_res_strand$signatures) <- c("Signature A", "Signature B")
#'
#' ## Generate the plot
#' plot_192_profile(nmf_res_strand$signatures)
#' @seealso
#' \code{\link{mut_matrix_stranded}},
#' \code{\link{extract_signatures}},
#' \code{\link{plot_96_profile}}
#'
#' @export
plot_192_profile <- function(mut_matrix, colors = NA, ymax = 0.2, condensed = FALSE) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
freq <- full_context <- substitution <- context <- strand <- full_context_strand <- NULL
# Check color vector length
# Colors for plotting
if (.is_na(colors)) {
colors <- COLORS6
}
if (length(colors) != 6) {
stop("Provide colors vector with length 6", call. = FALSE)
}
# Relative contribution
norm_mut_matrix <- apply(mut_matrix, 2, function(x) x / sum(x))
# Get substitution, context and strand from rownames. Then make long
tb <- norm_mut_matrix %>%
as.data.frame() %>%
tibble::rownames_to_column("full_context_strand") %>%
tidyr::separate(full_context_strand, into = c("full_context", "strand"), sep = "-") %>%
dplyr::mutate(
substitution = stringr::str_replace(full_context, "\\w\\[(.*)\\]\\w", "\\1"),
context = stringr::str_replace(full_context, "\\[.*\\]", "\\.")
) %>%
dplyr::select(-full_context) %>%
tidyr::pivot_longer(c(-substitution, -context, -strand), names_to = "sample", values_to = "freq") %>%
dplyr::mutate(sample = factor(sample, levels = unique(sample)))
# Change plotting parameters based on whether plot should be condensed.
if (condensed == TRUE) {
width <- 1
spacing <- 0
} else {
width <- 0.6
spacing <- 0.5
}
# Create plot. The warning about using alhpa as a discrete variable is muffled.
withCallingHandlers(
{
plot <- ggplot(data = tb, aes(
x = context,
y = freq,
fill = substitution,
width = width,
alpha = strand
)) +
geom_bar(stat = "identity", colour = "black", size = .2) +
scale_alpha_discrete(range = c(0.1, 1)) +
scale_fill_manual(values = colors) +
facet_grid(sample ~ substitution) +
ylab("Relative contribution") +
coord_cartesian(ylim = c(0, ymax)) +
scale_y_continuous(breaks = seq(0, ymax, 0.1)) +
guides(fill = "none") +
theme_bw() +
theme(
axis.title.y = element_text(size = 12, vjust = 1),
axis.text.y = element_text(size = 8),
axis.title.x = element_text(size = 12),
axis.text.x = element_text(size = 5, angle = 90, vjust = 0.5),
strip.text.x = element_text(size = 9),
strip.text.y = element_text(size = 9),
panel.grid.major.x = element_blank(),
panel.spacing.x = unit(spacing, "lines")
)
},
warning = function(w) {
if (grepl("Using alpha for a discrete variable is not advised.", conditionMessage(w))) {
invokeRestart("muffleWarning")
}
}
)
return(plot)
}
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