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#' Plot cosine similarity heatmap
#'
#' Plot pairwise cosine similarities in a heatmap.
#'
#'
#' @param cos_sim_matrix Matrix with pairwise cosine similarities.
#' Result from \code{\link{cos_sim_matrix}}
#' @param col_order Character vector with the desired order of the columns names for plotting. Optional.
#' @param row_order Character vector with the desired order of the row names for plotting. Optional.
#' @param cluster_rows Hierarchically cluster rows based on euclidean distance. Default = TRUE.
#' @param cluster_cols Hierarchically cluster cols based on euclidean distance. Default = FALSE.
#' @param method The agglomeration method to be used for hierarchical clustering. This should be one of
#' "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC)
#' or "centroid" (= UPGMC). Default = "complete".
#' @param plot_values Plot cosine similarity values in heatmap. Default = FALSE.
#'
#' @return Heatmap with cosine similarities
#'
#' @import ggplot2
#' @importFrom magrittr %>%
#' @examples
#'
#' ## See the 'mut_matrix()' example for how we obtained the mutation matrix:
#' mut_mat <- readRDS(system.file("states/mut_mat_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Get signatures
#' signatures <- get_known_signatures()
#'
#' ## Calculate the cosine similarity between each signature and each 96 mutational profile
#' cos_matrix <- cos_sim_matrix(mut_mat, signatures)
#'
#'
#' ## Plot the cosine similarity between each signature and each sample with hierarchical
#' ## clustering of samples and signatures.
#' plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE)
#'
#' ## In the above example, clustering is performed on the similarities of the samples with
#' ## the signatures. It's also possible to cluster the signatures and samples on their (96) profile.
#' ## This will generally give better results
#' ## If you use the same signatures for different analyses,
#' ## then their order will also be consistent.
#' hclust_cosmic <- cluster_signatures(signatures, method = "average")
#' cosmic_order <- colnames(signatures)[hclust_cosmic$order]
#' hclust_samples <- cluster_signatures(mut_mat, method = "average")
#' sample_order <- colnames(mut_mat)[hclust_samples$order]
#' ## Plot the cosine heatmap using this given signature order.
#' plot_cosine_heatmap(cos_matrix,
#' cluster_rows = FALSE, cluster_cols = FALSE,
#' row_order = sample_order, col_order = cosmic_order
#' )
#'
#' ## You can also plot the similarity of samples with eachother
#' cos_matrix <- cos_sim_matrix(mut_mat, mut_mat)
#' plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE)
#'
#'
#' ## It's also possible to add the actual values in the heatmap.
#' plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE, plot_values = TRUE)
#' @seealso
#' \code{\link{mut_matrix}},
#' \code{\link{cos_sim_matrix}}
#'
#' @export
plot_cosine_heatmap <- function(cos_sim_matrix, col_order = NA, row_order = NA, cluster_rows = TRUE,
cluster_cols = FALSE, method = "complete", plot_values = FALSE) {
# check explained argument
if (!inherits(cos_sim_matrix, "matrix")) {
stop("cos_sim_matrix must be a matrix")
}
# matrix should have row and colnames
if (length(colnames(cos_sim_matrix)) == 0) {
stop("cos_sim_matrix is missing colnames")
}
if (length(rownames(cos_sim_matrix)) == 0) {
stop("cos_sim_matrix is missing rownames")
}
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
Cosine.sim <- Signature <- Sample <- x <- y <- xend <- yend <- NULL
# If cluster_rows is TRUE perform clustering. Else use supplied row_order or
# the current column order.
if (!.is_na(row_order) & cluster_rows == TRUE) {
stop("row_order can only be provided when cluster_rows is FALSE", call. = FALSE)
} else if (!.is_na(row_order)) {
# check row_order argument
if (!inherits(row_order, "character")) {
stop("row_order must be a character vector", call. = FALSE)
}
if (length(row_order) != nrow(cos_sim_matrix)) {
stop("row_order must have the same length as the number of
samples in the explained matrix", call. = FALSE)
}
} else if (cluster_rows == TRUE) {
# cluster samples based on euclidean distance between relative contribution
hc.sample <- hclust(dist(cos_sim_matrix), method = method)
# order samples according to clustering
row_order <- rownames(cos_sim_matrix)[hc.sample$order]
dhc <- as.dendrogram(hc.sample)
# rectangular lines
ddata <- ggdendro::dendro_data(dhc, type = "rectangle")
# plot dendrogram of hierachical clustering
dendrogram_rows <- ggplot(ggdendro::segment(ddata)) +
geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
coord_flip() +
scale_y_reverse(expand = c(0.2, 0)) +
ggdendro::theme_dendro()
}
else {
row_order <- rownames(cos_sim_matrix)
}
# If cluster_cols is TRUE perform clustering. Else use supplied col_order or
# the current column order.
if (!.is_na(col_order) & cluster_cols == TRUE) {
stop("col_order can only be provided when cluster_cols is FALSE", call. = FALSE)
} else if (!.is_na(col_order)) {
# check col_order argument
if (!inherits(col_order, "character")) {
stop("col_order must be a character vector", call. = FALSE)
}
if (length(col_order) != ncol(cos_sim_matrix)) {
stop("col_order must have the same length as the number of
signatures in the explained matrix", call. = FALSE)
}
} else if (cluster_cols == TRUE) {
# Cluster cols
hc.sample2 <- cos_sim_matrix %>%
t() %>%
dist() %>%
hclust(method = method)
col_order <- colnames(cos_sim_matrix)[hc.sample2$order]
dhc <- as.dendrogram(hc.sample2)
# rectangular lines
ddata <- ggdendro::dendro_data(dhc, type = "rectangle")
# plot dendrogram of hierachical clustering
dendrogram_cols <- ggplot(ggdendro::segment(ddata)) +
geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
ggdendro::theme_dendro() +
scale_y_continuous(expand = c(0.2, 0))
} else {
col_order <- colnames(cos_sim_matrix)
}
# Make matrix long and set factor levels, to get the correct order for plotting.
cos_sim_matrix.m <- cos_sim_matrix %>%
as.data.frame() %>%
tibble::rownames_to_column("Sample") %>%
tidyr::pivot_longer(-Sample, names_to = "Signature", values_to = "Cosine.sim") %>%
dplyr::mutate(
Signature = factor(Signature, levels = col_order),
Sample = factor(Sample, levels = row_order)
)
# plot heatmap
heatmap <- ggplot(cos_sim_matrix.m, aes(x = Signature, y = Sample, fill = Cosine.sim, order = Sample)) +
geom_raster() +
scale_fill_distiller(palette = "YlGnBu", direction = 1, name = "Cosine \nsimilarity", limits = c(0, 1.000000001)) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 8),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
labs(x = NULL, y = NULL)
# if plot_values is TRUE, add values to heatmap
if (plot_values == TRUE) {
heatmap <- heatmap + geom_text(aes(label = round(Cosine.sim, 2)), size = 2)
}
# Add dendrogram depending on the clustering of the rows and the columns.
if (cluster_rows == TRUE & cluster_cols == TRUE) {
empty_fig <- ggplot() +
theme_void()
plot_final <- cowplot::plot_grid(empty_fig, dendrogram_cols, dendrogram_rows, heatmap,
align = "hv", axis = "tblr", rel_widths = c(0.3, 1), rel_heights = c(0.3, 1)
)
}
else if (cluster_rows == TRUE & cluster_cols == FALSE) {
# combine plots
plot_final <- cowplot::plot_grid(dendrogram_rows, heatmap, align = "h", rel_widths = c(0.3, 1))
} else if (cluster_rows == FALSE & cluster_cols == TRUE) {
plot_final <- cowplot::plot_grid(dendrogram_cols, heatmap, align = "v", rel_heights = c(0.3, 1)) +
# reverse order of the samples such that first is up
ylim(rev(levels(factor(cos_sim_matrix.m$Sample))))
} else {
plot_final <- heatmap +
# reverse order of the samples such that first is up
ylim(rev(levels(factor(cos_sim_matrix.m$Sample))))
}
return(plot_final)
}
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