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#' Plot signature strand bias
#'
#' Plot strand bias per mutation type for each signature.
#'
#' @param signatures_strand_bias Signature matrix with 192 features
#' @return Barplot
#'
#' @import ggplot2
#' @importFrom magrittr %>%
#'
#' @examples
#' ## See the 'mut_matrix()' example for how we obtained the following
#' ## mutation matrix.
#' mut_mat_s <- readRDS(system.file("states/mut_mat_s_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Extracting signatures can be computationally intensive, so
#' ## we use pre-computed data generated with the following command:
#' # nmf_res_strand <- extract_signatures(mut_mat_s, rank = 2)
#'
#' nmf_res_strand <- readRDS(system.file("states/nmf_res_strand_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Provide column names for the plot.
#' colnames(nmf_res_strand$signatures) <- c("Signature A", "Signature B")
#'
#' ## Creat figure
#' plot_signature_strand_bias(nmf_res_strand$signatures)
#'
#' ## You can also plot the bias of samples
#' plot_signature_strand_bias(mut_mat_s[, c(1, 2)])
#' @seealso
#' \code{link{extract_signatures}},
#' \code{link{mut_matrix}}
#'
#' @export
plot_signature_strand_bias <- function(signatures_strand_bias) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
Signature <- type <- ratio <- transcribed <- untranscribed <- NULL
`Group.1` <- `Group.2` <- significant <- pval <- log2_ratio <- NULL
# check if there are 192 features in the signatures
if (dim(signatures_strand_bias)[1] != 192) {
stop(paste(
"Input signature matrix does not have 192 features (96",
"trinucleotide * 2 strands)."
))
}
# aggregate by strand and type
sum_per_type <- stats::aggregate(signatures_strand_bias,
by = list(STRAND, SUBSTITUTIONS_192),
FUN = sum
)
# Make tibble longer
stats_per_type <- sum_per_type %>%
dplyr::rename(strand = `Group.1`, type = `Group.2`) %>%
dplyr::mutate(strand = ifelse(strand == "T", "transcribed", "untranscribed")) %>%
tidyr::pivot_longer(c(-strand, -type), names_to = "Signature") %>% # Combine signature columns
tidyr::pivot_wider(names_from = strand) %>% # Split transcribed/untranscribed column for binom test.
dplyr::mutate(
observed = as.integer(transcribed),
size = as.integer(transcribed + untranscribed),
ratio = (transcribed / untranscribed),
ratio = ifelse(is.nan(ratio), 1, ratio),
log2_ratio = log2(ratio)
)
# Perform binomial test
stats_per_type$pval <- purrr::map_dbl(seq_len(nrow(stats_per_type)), function(i) {
row <- stats_per_type[i, ]
binomial_test(0.5, row$size, row$observed)$pval
})
# Add stars when significant.
stats_per_type <- dplyr::mutate(stats_per_type, significant = ifelse(pval < 0.05, "*", " "))
# Find maximum y value for plotting
ratios <- stats_per_type %>%
dplyr::filter(is.finite(log2_ratio)) %>%
dplyr::pull(log2_ratio)
max <- round(max(abs(ratios)))
# Plot
plot <- ggplot(
stats_per_type,
aes(x = type, y = log2_ratio, fill = type)
) +
geom_bar(stat = "identity", position = "dodge", color = "black") +
scale_y_continuous(limits = c(-max, max)) +
scale_fill_manual(values = COLORS6) +
facet_grid(Signature ~ .) +
ylab("log2(transcribed/untranscribed)") +
theme_bw() +
scale_x_discrete(breaks = NULL) +
xlab("") +
geom_text(
aes(
x = type,
y = log2(ratio),
label = significant,
vjust = ifelse(sign(log2_ratio) > 0, 0.5, 1)
),
size = 8, position = ggplot2::position_dodge(width = 1)
)
return(plot)
}
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