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#' Compare two 96 mutation profiles
#'
#' Plots two 96 mutation profiles and their difference, reports the residual
#' sum of squares (RSS).
#'
#' @param profile1 First 96 mutation profile
#' @param profile2 Second 96 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(-0.02, 0.02)
#' @param colors 6 value color vector
#' @param condensed More condensed plotting format. Default = F.
#' @return 96 spectrum plot of profile 1, profile 2 and their difference
#'
#' @import ggplot2
#'
#' @examples
#' ## See the 'mut_matrix()' example for how we obtained the following
#' ## mutation matrix.
#' mut_mat <- readRDS(system.file("states/mut_mat_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Extracting signatures can be computationally intensive, so
#' ## we use pre-computed data generated with the following command:
#' # nmf_res <- extract_signatures(mut_mat, rank = 2)
#'
#' nmf_res <- readRDS(system.file("states/nmf_res_data.rds",
#' package = "MutationalPatterns"
#' ))
#'
#' ## Compare the reconstructed 96-profile of sample 1 with the original profile
#' ## The same thing could be done with a reconstructed profile from signature refitting.
#' plot_compare_profiles(mut_mat[, 1],
#' nmf_res$reconstructed[, 1],
#' profile_names = c("Original", "Reconstructed")
#' )
#'
#' ## You could also compare regular mutation profiles with eachother.
#' plot_compare_profiles(
#' mut_mat[, 1],
#' mut_mat[, 2]
#' )
#'
#'
#' ## You can also change the y limits.
#' ## This can be done separately for the profiles and the different facets.
#' plot_compare_profiles(mut_mat[, 1],
#' mut_mat[, 2],
#' profile_ymax = 0.3,
#' diff_ylim = c(-0.03, 0.03)
#' )
#' @seealso
#' \code{\link{mut_matrix}},
#' \code{\link{extract_signatures}},
#' \code{\link{plot_compare_indels}},
#' \code{\link{plot_compare_dbs}},
#' \code{\link{plot_compare_mbs}}
#'
#' @export
#'
plot_compare_profiles <- function(profile1,
profile2,
profile_names = c("profile 1", "profile 2"),
profile_ymax = 0.2,
diff_ylim = c(-0.02, 0.02),
colors = NA,
condensed = FALSE) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
value <- substitution <- Sample <- Contribution <- Signature <- variable <- NULL
full_context <- context <- NULL
# if colors parameter not provided, set to default colors
if (.is_na(colors)) {
colors <- COLORS6
}
# Create a comparison of the profiles.
comp <- .create_profile_comparison(profile1, profile2, profile_names)
# Get substitution and context. Then make data long for plotting.
df <- comp$matrix %>%
as.data.frame() %>%
tibble::rownames_to_column("full_context") %>%
dplyr::mutate(
substitution = stringr::str_replace(full_context, "\\w\\[(.*)\\]\\w", "\\1"),
context = stringr::str_replace(full_context, "\\[.*\\]", "\\.")
) %>%
dplyr::select(-full_context) %>%
tidyr::pivot_longer(c(-substitution, -context), names_to = "sample", values_to = "value") %>%
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(df[, c("substitution", "context")], profile_names, profile_ymax, diff_ylim)
# Plotting parameters
if (condensed == TRUE) {
width <- 1
spacing <- 0
} else {
width <- 0.6
spacing <- 0.5
}
# Create plot
plot <- ggplot(data = df, aes(
x = context,
y = value,
fill = substitution,
width = width
)) +
geom_bar(
stat = "identity",
position = "identity",
colour = "black", size = .2
) +
geom_blank(data = df_blank, aes(x = context, y = value)) +
scale_fill_manual(values = colors) +
facet_grid(sample ~ substitution, scales = "free_y") +
labs(
y = "Relative contribution",
title = comp$title
) +
guides(fill = "none") +
theme_bw() +
theme(
axis.title.y = element_text(size = 12, vjust = 1),
axis.text.y = element_text(size = 8),
axis.title.x = element_text(size = 12),
axis.text.x = element_text(size = 5, angle = 90, vjust = 0.5),
strip.text.x = element_text(size = 14),
strip.text.y = element_text(size = 14),
panel.grid.major.x = element_blank(),
panel.spacing.x = unit(spacing, "lines")
)
return(plot)
}
#' Create a relative comparison between two profiles.
#'
#' Create a matrix with the relative profiles and the difference.
#'
#' @param profile1 First mutation profile
#' @param profile2 Second mutation profile
#' @param profile_names Character vector with names of the mutations profiles
#' used for plotting
#'
#' @return matrix with the relative profiles and the difference
#' @noRd
#'
.create_profile_comparison <- function(profile1, profile2, profile_names) {
s1_relative <- profile1 / sum(profile1)
s2_relative <- profile2 / sum(profile2)
diff <- s1_relative - s2_relative
# residual sum of squares
RSS <- sum(diff^2)
RSS <- format(RSS, scientific = TRUE, digits = 3)
# calculate cosine similarity between the two profiles
cosine_sim <- cos_sim(profile1, profile2)
# round
cosine_sim <- round(cosine_sim, 3)
# Create title
title <- paste0("RSS = ", RSS, "; Cosine similarity = ", cosine_sim)
# Combine samples and diff.
x <- BiocGenerics::cbind(s1_relative, s2_relative, diff)
colnames(x) <- c(profile_names, "Difference")
res <- list("title" = title, "matrix" = x)
return(res)
}
#' Create a dummy data frame with y-axis limits
#'
#' This functions creates dummy dataframe with y-axis limits for the relative profiles,
#' as well as the diff profile. The result can be used by ggplot to change the y axis
#' separately for the profiles and diffs.
#'
#' @param df Dataframe with the mutation types
#' @param profile_names Character vector with names of the mutations profiles
#' used for plotting
#' @param profile_ymax Maximum value of y-axis (relative contribution) for
#' profile plotting
#' @param diff_ylim Y-axis limits for profile difference plot
#'
#' @return Dataframe with y-axis limits
#' @noRd
#' @importFrom magrittr %>%
#'
.create_dummy_limits <- function(df, profile_names, profile_ymax, diff_ylim) {
df_dummy <- df[c(1, 1, 1, 1), ] %>%
dplyr::mutate(
sample = c(profile_names, "Difference", "Difference"),
sample = factor(sample, levels = unique(sample)),
value = c(profile_ymax, profile_ymax, diff_ylim[1], diff_ylim[2])
)
return(df_dummy)
}
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