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#' Compare two indel mutation profiles
#'
#' Plots two indel mutation profiles and their difference, reports the residual
#' sum of squares (RSS).
#'
#' @param profile1 First mutation profile
#' @param profile2 Second mutation profile
#' @param profile_names Character vector with names of the mutations profiles
#' used for plotting, default = c("profile 1", "profile 2")
#' @param profile_ymax Maximum value of y-axis (relative contribution) for
#' profile plotting. This can only be used to increase the y axis.
#' If bars fall outside this limit, the maximum value is
#' automatically increased. default = 0.2.
#' @param diff_ylim Y-axis limits for profile difference plot,
#' default = c(-0.1, 0.1)
#'
#' @return A ggplot2 object
#' @export
#' @family Indels
#' @seealso \code{\link{plot_compare_profiles}},
#' \code{\link{plot_compare_dbs}},
#' \code{\link{plot_compare_mbs}}
#' @examples
#'
#' ## Get the indel counts
#' ## See 'count_indel_contexts()' for more info on how to do this.
#' indel_counts <- readRDS(system.file("states/blood_indel_counts.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Get indel refit info.
#' ## See 'fit_to_signatures()' for more info on how to do this.
#' fit_res <- readRDS(system.file("states/indel_refit.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Compare the reconstructed profile of sample 1 with the original profile
#' ## The same thing could be done with a reconstructed profile from NMF.
#' plot_compare_indels(indel_counts[, 1], fit_res$reconstructed[, 1])
#'
#' ## You could also compare regular mutation profiles with eachother.
#' plot_compare_indels(
#' indel_counts[, 1],
#' indel_counts[, 2]
#' )
#'
#' ## Or change the names of the profiles
#' plot_compare_indels(indel_counts[, 1],
#' indel_counts[, 2],
#' profile_names = c("Original", "Reconstructed")
#' )
#'
#' ## You can also change the y limits.
#' ## This can be done separately for the profiles and the different facets.
#' plot_compare_indels(indel_counts[, 1],
#' indel_counts[, 2],
#' profile_ymax = 0.3,
#' diff_ylim = c(-0.03, 0.03)
#' )
plot_compare_indels <- function(profile1, profile2,
profile_names = c("profile 1", "profile 2"),
profile_ymax = 0.2,
diff_ylim = c(-0.1, 0.1)) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
count <- muttype <- muttype_sub <- muttype_total <- value <- NULL
# Create a comparison of the profiles.
comp <- .create_profile_comparison(profile1, profile2, profile_names)
# Separate muttype and muttype_sub. Then make data long
counts <- comp$matrix %>%
as.data.frame() %>%
tibble::rownames_to_column("muttype_total") %>%
tidyr::separate(muttype_total, c("muttype", "muttype_sub"), sep = "_(?=[0-9])") %>%
dplyr::mutate(muttype = factor(muttype, levels = unique(muttype))) %>%
tidyr::gather(key = "sample", value = "count", -muttype, -muttype_sub) %>%
dplyr::mutate(sample = factor(sample, levels = unique(sample)))
# Add dummy non_visible data points to force y axis limits per facet
df_blank <- .create_dummy_limits(counts[, c("muttype", "muttype_sub")], profile_names, profile_ymax, diff_ylim)
# Set facet names
facet_labs_x <- c("1: C", "1: T", "1: C", "1: T", 2, 3, 4, "5+", 2, 3, 4, "5+", 2, 3, 4, "5+")
names(facet_labs_x) <- levels(counts$muttype)
# Set colors
colors <- c(
"#FDBE6F", "#FF8001", "#B0DD8B", "#36A12E", "#FDCAB5", "#FC8A6A",
"#F14432", "#BC141A", "#D0E1F2", "#94C4DF", "#4A98C9", "#1764AB",
"#E2E2EF", "#B6B6D8", "#8683BD", "#61409B"
)
# Create plot
fig <- ggplot(counts, aes(x = muttype_sub, y = count, fill = muttype)) +
geom_bar(stat = "identity") +
geom_blank(data = df_blank, aes(x = muttype_sub, y = value)) +
facet_grid(sample ~ muttype,
scales = "free",
space = "free_x",
labeller = labeller(muttype = facet_labs_x)
) +
scale_fill_manual(values = colors) +
labs(fill = "Mutation type", title = comp$title, y = "Relative contribution", x = "") +
theme_minimal() +
theme(panel.grid.major.x = element_blank(), strip.background = element_rect(fill = "cadetblue"))
return(fig)
}
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