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#' Plot a riverplot
#'
#' Function to plot a SNV mutation matrix as a riverplot.
#' This is especially useful when looking at a wide
#' mutational context
#'
#' @param mut_matrix Matrix containing mutation counts.
#' @param condensed More condensed plotting format. Default = F.
#'
#' @return A ggplot object
#'
#' @export
#' @import ggplot2
#' @import ggalluvial
#'
#' @seealso
#' \code{\link{mut_matrix}},
#' \code{\link{plot_96_profile}},
#' \code{\link{plot_profile_heatmap}}
#'
#' @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_river(mut_mat[,c(1,4)])
#'
#' ## Get extended matrix
#' mut_mat_extended <- readRDS(system.file("states/mut_mat_data_extended.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Create heatmap of extended profile
#' plot_river(mut_mat_extended[,c(1,4)])
#'
#' ## Create condensed version of riverplot
#' plot_river(mut_mat_extended[,c(1,4)], condensed = TRUE)
#'
plot_river = function(mut_matrix, condensed = FALSE){
context_m <- .river_create_context_matrix(mut_matrix)
lodes_tb <- .river_create_lodes(mut_matrix, context_m)
fig <- .river_plot_river(lodes_tb, mut_matrix, condensed)
return(fig)
}
#' Split the contexts of a mutation matrix
#'
#' Function to split the contexts of a mutation matrix
#' in multiple columns.
#'
#' @param mut_matrix Matrix containing mutation counts.
#'
#' @return Matrix containing the contexts of a mutation matrix.
#' @noRd
#' @importFrom magrittr %>%
#'
.river_create_context_matrix = function(mut_matrix){
# Split left and right context from mutations
context_m <- mut_matrix %>%
rownames() %>%
stringr::str_split("\\[|\\]", simplify = TRUE)
# Split contexts into single bases
left_m <- stringr::str_split(context_m[,1], "", simplify = TRUE)
right_m <- stringr::str_split(context_m[,3], "", simplify = TRUE)
# Combine all
context_m <- cbind(left_m, context_m[,2], right_m)
# Set column names
mut_positions <- c((-1*rev(seq_len(ncol(left_m)))), 0, seq_len(ncol(right_m)))
colnames(context_m) <- paste0("pos_", mut_positions)
return(context_m)
}
#' Create a long format lodes tibble.
#'
#' @param mut_matrix Matrix containing mutation counts.
#' @param context_m Matrix containing the contexts of a mutation matrix.
#'
#' @return A tibble in lodes format containing the mutation contexts.
#' @noRd
#'
#' @importFrom magrittr %>%
#'
.river_create_lodes = function(mut_matrix, context_m){
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
pos <- type <- sample_name <- rowid <- nrmuts <- NULL
mut_df <- as.data.frame(mut_matrix)
context_df <- as.data.frame(context_m)
# Transform data into long (lodes) format.
lodes_tb <- cbind(mut_df, context_df) %>%
tibble::rowid_to_column() %>%
tidyr::pivot_longer(cols = dplyr::contains("pos_"), names_to = "pos", values_to = "type") %>%
tidyr::pivot_longer(cols = c(-pos, -type, -rowid),
names_to = "sample_name",
values_to = "nrmuts") %>%
dplyr::mutate(pos = stringr::str_remove(pos, "pos_"),
pos = factor(pos, levels = unique(pos)),
type = factor(type, levels = unique(type)),
sample_name = factor(sample_name, levels = unique(sample_name)))
return(lodes_tb)
}
#' Plot a riverplot with a lodes tibble as input
#'
#' @param lodes_tb A tibble in lodes format containing the mutation contexts.
#' @param condensed More condensed plotting format.
#'
#' @return A ggplot object
#' @noRd
#'
#' @import ggplot2
#' @import ggalluvial
#'
.river_plot_river = function(lodes_tb, mut_matrix, condensed){
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
rowid <- nrmuts <- NULL
# Set colours
colours_v <- c("#EF2D56", "#446DF6","#6FAB94", "#FFBC63",
"#2EBAED", "#000000", "#DE1C14", "#D4D2D2",
"#ADCC54", "#F0D0CE")
types <- c("T", "C", "A", "G", "C>A", "C>G", "C>T", "T>A", "T>C", "T>G")
typesin <- types %in% unique(lodes_tb$type)
used_colours <- colours_v[typesin]
# Change plotting parameters based on whether plot should be condensed.
if (condensed == TRUE) {
spacing <- 0
} else {
spacing <- 0.5
}
# Create facet texts
nr_muts <- colSums(mut_matrix)
facet_labs_y <- stringr::str_c(colnames(mut_matrix), " (n = ", nr_muts, ")")
names(facet_labs_y) <- colnames(mut_matrix)
#Add stratum stat.
StatStratum <- ggalluvial::StatStratum
# Create plot
fig <- ggplot(lodes_tb, aes(x = pos,
stratum = type,
y = nrmuts,
alluvium = rowid,
fill = type,
label = type)) +
geom_stratum() +
geom_flow() +
geom_text(stat = StatStratum, size = 3, colour = "white") +
facet_grid(sample_name ~ ., scales = "free_y",
labeller = labeller(sample_name = facet_labs_y)) +
scale_fill_manual(values = used_colours) +
labs(x = "Position", y = "Nr mutations") +
theme_bw() +
theme(panel.spacing.y = unit(spacing, "lines"))
return(fig)
}
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