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#' Plot a mutation matrix as a heatmap
#'
#' Function to plot a SNV mutation matrix as a heatmap.
#' This is especially useful when looking at a wide mutational context.
#'
#'
#' @param mut_matrix Matrix containing mutation counts.
#' @param by Optional grouping variable
#' @param max Maximum value used for plotting the relative contributions.
#' Contributions that are higher will have the maximum colour. (Default: 0.02)
#' @param condensed More condensed plotting format. Default = F.
#'
#' @return A ggplot object
#' @export
#' @importFrom magrittr %>%
#' @import ggplot2
#'
#' @seealso
#' \code{\link{mut_matrix}},
#' \code{\link{plot_96_profile}},
#' \code{\link{plot_river}}
#' @examples
#'
#' ## See the 'mut_matrix()' examples for how we obtained the
#' ## mutation matrix information:
#' ## Get regular matrix
#' mut_mat <- readRDS(system.file("states/mut_mat_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Create heatmap of profile
#' plot_profile_heatmap(mut_mat, max = 0.1)
#'
#' ## Get extended matrix
#' mut_mat_extended <- readRDS(system.file("states/mut_mat_data_extended.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Create heatmap of extended profile
#' plot_profile_heatmap(mut_mat_extended)
#'
#' ## Or plot heatmap per tissue
#' tissue <- c(
#' "colon", "colon", "colon",
#' "intestine", "intestine", "intestine",
#' "liver", "liver", "liver"
#' )
#'
#' plot_profile_heatmap(mut_mat_extended, by = tissue)
#'
#' ## Or plot the heatmap per sample.
#' plot_profile_heatmap(mut_mat_extended,
#' by = colnames(mut_mat_extended),
#' max = 0.05
#' )
#'
#'
#' ## Create a condensed heatmap of extended profile
#' plot_profile_heatmap(mut_mat_extended, condensed = TRUE)
plot_profile_heatmap <- function(mut_matrix,
by = NA,
max = 0.02,
condensed = FALSE) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
fullcontext <- l_context <- r_context <- muttype <- NULL
nrmuts <- rel_nrmuts <- NULL
# check arguments
if (!inherits(mut_matrix, "matrix")) {
stop("mut_matrix must be a matrix", call. = FALSE)
}
# matrix should have row and colnames
if (length(colnames(mut_matrix)) == 0) {
stop("mut_matrix is missing colnames", call. = FALSE)
}
if (length(rownames(mut_matrix)) == 0) {
stop("mut_matrix is missing rownames", call. = FALSE)
}
# Transform the data into long format and get left/right context seperately.
tb <- mut_matrix %>%
as.data.frame() %>%
tibble::rownames_to_column("fullcontext") %>%
tidyr::pivot_longer(-fullcontext, names_to = "sample", values_to = "nrmuts") %>%
tidyr::separate("fullcontext", # Separate context into left context, mut and right context
into = c("l_context", "muttype", "r_context"),
sep = "\\[|\\]"
) %>%
dplyr::mutate(
mut = factor(muttype, levels = unique(muttype)),
r_context = factor(r_context, levels = unique(r_context)),
l_context = factor(l_context, levels = Biostrings::reverse(unique(l_context)))
)
# Make data relative
tb <- tb %>%
dplyr::group_by(sample) %>%
dplyr::mutate(rel_nrmuts = nrmuts / sum(nrmuts)) %>%
dplyr::ungroup()
# Add sample grouping
if (.is_na(by)) {
by <- "all"
}
tb_by <- tibble::tibble(
"sample" = unique(tb$sample),
"by" = by
)
tb <- tb %>%
dplyr::left_join(tb_by, by = "sample")
# Combine samples based on grouping
tb <- tb %>%
dplyr::mutate(by = factor(by, levels = unique(by))) %>%
dplyr::group_by(by, l_context, muttype, r_context) %>%
dplyr::summarise(
nrmuts = sum(nrmuts),
rel_nrmuts = mean(rel_nrmuts),
total_indv = dplyr::n(),
.groups = "drop_last"
) %>%
dplyr::ungroup()
# If value is higher than y_max, change it to ymax. (Prevents plotting issues)
tb <- tb %>%
dplyr::mutate(rel_nrmuts = ifelse(rel_nrmuts > max, max, rel_nrmuts))
# Count number muts per sample_group
tot_muts_samplegroups_tb <- tb %>%
dplyr::group_by(by) %>%
dplyr::summarise(nrmuts = sum(nrmuts), .groups = "drop_last")
# Create y facet labels
facet_labs_y <- stringr::str_c(tot_muts_samplegroups_tb$by, " (n = ", tot_muts_samplegroups_tb$nrmuts, ")")
names(facet_labs_y) <- tot_muts_samplegroups_tb$by
# Count number muts per mut
tot_muts_muttype_tb <- tb %>%
dplyr::group_by(muttype) %>%
dplyr::summarise(nrmuts = sum(nrmuts), .groups = "drop_last")
# Create x facet labels
facet_labs_x <- stringr::str_c(tot_muts_muttype_tb$muttype, " (n = ", tot_muts_muttype_tb$nrmuts, ")")
names(facet_labs_x) <- tot_muts_muttype_tb$muttype
# Set plotting parameters
context_size <- stringr::str_length(tb$l_context[1])
if (context_size == 1) {
axis_size <- 10
} else if (context_size == 2) {
axis_size <- 8
} else if (context_size == 3) {
axis_size <- 4
} else {
axis_size <- 3
}
# Change plotting parameters based on whether plot should be condensed.
if (condensed == TRUE) {
spacing <- 0
} else {
spacing <- 0.5
}
# Create plot
fig <- ggplot(tb, aes(x = r_context,
y = l_context,
fill = rel_nrmuts)) +
geom_raster() +
scale_fill_distiller(
palette = "YlGnBu",
direction = 1,
name = "Relative contribution",
limits = c(0, max)
) +
facet_grid(by ~ muttype, labeller = labeller(by = facet_labs_y, muttype = facet_labs_x)) +
labs(x = "3' context", y = "5' context") +
theme_bw() +
theme(
axis.text.y = element_text(size = axis_size),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = axis_size),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.spacing.x = unit(spacing, "lines"),
panel.spacing.y = unit(spacing, "lines")
)
return(fig)
}
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