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#' Function to perform the strict signature refitting for a single sample with backwards selection
#'
#' @param mut_mat_sample mutation count matrix for a single sample
#' @param my_signatures signature matrix
#' @param max_delta The maximum difference in original vs reconstructed cosine similarity between two iterations.
#'
#' @return A list containing a fit_res object, similar to 'fit_to_signatures' and a ggplot graph
#' that for each sample shows in what order the signatures were removed and how this affected the cosine similarity.
#' @noRd
#'
.strict_refit_backwards_selection_sample = function(mut_mat_sample, my_signatures, max_delta){
# Determine the number of signatures
nsigs <- ncol(my_signatures)
# Fit again
fit_res <- fit_to_signatures(mut_mat_sample, my_signatures)
sim <- .get_cos_sim_ori_vs_rec(mut_mat_sample, fit_res)
# Keep track of the cosine similarity and which signatures are removed.
sims <- vector("list", nsigs)
sims[[1]] <- sim
removed_sigs <- vector("list", nsigs)
removed_sigs[[1]] <- "None"
if (nsigs > 1){ # Only remove signatures if there is more than 1.
# Sequentially remove the signature with the lowest contribution
for (j in seq(2, nsigs)) {
# Remove signature with the weakest relative contribution
contri_order <- fit_res$contribution %>%
prop.table(2) %>%
rowSums() %>%
order()
weakest_sig_index <- contri_order[1]
weakest_sig <- colnames(my_signatures)[weakest_sig_index]
removed_sigs[[j]] <- weakest_sig
signatures_sel <- my_signatures[, -weakest_sig_index, drop = FALSE]
# Fit with new signature selection
fit_res <- fit_to_signatures(mut_mat_sample, signatures_sel)
sim_new <- .get_cos_sim_ori_vs_rec(mut_mat_sample, fit_res)
if (is.nan(sim_new) == TRUE) {
sim_new <- 0
warning("New similarity between the original and the reconstructed
spectra after the removal of a signature was NaN.
It has been converted into a 0.
This happened with the following fit_res:")
print(fit_res)
}
sims[[j]] <- sim_new
# Check if the loss in cosine similarity between the original vs reconstructed after removing the signature is below the cutoff.
delta <- sim - sim_new
if (delta <= max_delta) {
my_signatures <- signatures_sel
sim <- sim_new
}
else {
break
}
}
}
# Plot how the cosine similarities decayed
sim_decay_fig <- .plot_sim_decay(sims, removed_sigs, max_delta, "backwards")
# Perform final fit on selected signatures
fit_res <- fit_to_signatures(mut_mat_sample, my_signatures)
# Add data of sample to list.
results <- list("sim_decay_fig" = sim_decay_fig, "fit_res" = fit_res)
return(results)
}
#' Get the cosine similarity between a reconstructed mutation matrix and the original
#'
#' @param mut_matrix mutation count matrix (dimensions: x mutation types
#' X n samples)
#' @param fit_res Named list with signature contributions and reconstructed
#' mutation matrix
#'
#' @return Cosine similarity
#' @noRd
#'
.get_cos_sim_ori_vs_rec <- function(mut_matrix, fit_res) {
cos_sim_all <- cos_sim_matrix(mut_matrix, fit_res$reconstructed)
cos_sim <- diag(cos_sim_all)
mean_cos_sim <- mean(cos_sim)
return(mean_cos_sim)
}
#' Plot decay in cosine similarity as signatures are removed.
#'
#' @param sims List of cosine similarities
#' @param removed_sigs List of iteratively removed signatures
#' @param max_delta The maximum difference in original vs reconstructed cosine similarity.
#' @param method The signature selection method that was used. Possible values:
#' * 'backwards';
#' * 'best_subset';
#'
#' @import ggplot2
#' @importFrom magrittr %>%
#' @noRd
#' @return ggplot object
#'
.plot_sim_decay <- function(sims, removed_sigs, max_delta, method = c("backwards", "best_subset")) {
# These variables use non standard evaluation.
# To avoid R CMD check complaints we initialize them to NULL.
Removed_signatures <- Cosine_similarity <- NULL
# Match argument
method = match.arg(method)
#Remove NULL values
sims <- Filter(Negate(is.null), sims)
removed_sigs <- Filter(Negate(is.null), removed_sigs)
# Prepare data
sims <- sims[!S4Vectors::isEmpty(sims)] %>%
unlist()
removed_sigs <- removed_sigs[!S4Vectors::isEmpty(removed_sigs)] %>%
unlist()
tb <- tibble::tibble(
"Cosine_similarity" = sims,
"Removed_signatures" = factor(removed_sigs, levels = removed_sigs)
)
# Determine if the final removed signature exceeded the cutoff.
sims_l <- length(sims)
col <- rep("low_delta", sims_l)
if (sims_l > 1){ # Check if any signatures have been removed, before calculating the delta.
final_delta <- sims[sims_l - 1] - sims[sims_l]
if (final_delta > max_delta) {
col[sims_l] <- "high_delta"
}
}
# Set the x-axis label and theme
if (method == "backwards"){
xlab <- "Removed signatures"
my_theme <- theme(
axis.text.x = element_text(angle = 90, size = 10, hjust = 1, vjust = 0.5),
text = element_text(size = 12)
)
} else{
xlab <- "Nr. signatures used"
my_theme <- theme(text = element_text(size = 12))
}
# Create plot
fig <- ggplot(data = tb, aes(x = Removed_signatures, y = Cosine_similarity, fill = col)) +
geom_bar(stat = "identity") +
scale_fill_manual(
limits = c("low_delta", "high_delta"),
values = c("grey", "red"),
guide = "none"
) +
labs(
x = xlab,
y = paste0("Cosine similarity (max delta: ", max_delta, ")")
) +
theme_classic() +
my_theme
return(fig)
}
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