comparison mutational_patterns.R @ 3:e332cf9dfa06 draft

"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/mutational_patterns commit 0f7593f703ba0dfd12aea1c0460e371f08b57d2f"

2:aea952be68cb

    default = NA,
    type = 'character',
    help = "path to the tab separated file describing the levels in function of datasets"
  ),
  make_option(
    "--signum",
    default = 2,
    type = 'integer',
    help = "selects the N most significant signatures in samples to express mutational patterns"
  ),

    "--newsignum",
    default = 2,
    type = 'integer',
    help = "Number of new signatures to be captured"
  ),

  make_option(
    "--output_spectrum",
    default = NA,
    type = 'character',
    help = "path to output dataset"

    default = NA,
    type = 'character',
    help = "path to output dataset"
  ),
  make_option(
    "--output_cosmic",
    default = NA,
    type = 'character',
    help = "path to output dataset"
  )
)

opt = parse_args(OptionParser(option_list = option_list),
                 args = commandArgs(trailingOnly = TRUE))

################ Manage input data ####################
json_dict <- opt$inputs
parser <- newJSONParser()
parser$addData(json_dict)
fileslist <- parser$getObject()
vcf_files <- attr(fileslist, "names")
sample_names <- unname(unlist(fileslist))
ref_genome <- opt$genome
vcf_table <- data.frame(sample_name=sample_names, path=vcf_files)

library(MutationalPatterns)
library(ref_genome, character.only = TRUE)

# Load the VCF files into a GRangesList:
vcfs <- read_vcfs_as_granges(vcf_files, sample_names, ref_genome)
if (!is.na(opt$levels)[1]) {  # manage levels if there are
    levels_table  <- read.delim(opt$levels, header=FALSE, col.names=c("sample_name","level"))
    library(dplyr)
    metadata_table <- left_join(vcf_table, levels_table, by = "sample_name")
#    detach("package:dplyr", unload=TRUE)
    tissue <- metadata_table$level
}

##### This is done for any section ######
mut_mat <- mut_matrix(vcf_list = vcfs, ref_genome = ref_genome)


###### Section 1 Mutation characteristics and spectrums #############
if (!is.na(opt$output_spectrum)[1]) {
    pdf(opt$output_spectrum, paper = "special", width = 11.69, height = 11.69)
    type_occurrences <- mut_type_occurrences(vcfs, ref_genome)

    
    if (is.na(opt$levels)[1]) {
        p1 <- plot_spectrum(type_occurrences, CT = TRUE, legend=TRUE)
        plot(p1)
    } else {
        p2 <- plot_spectrum(type_occurrences, by = tissue, CT=TRUE, legend=TRUE) # by sample
        p3 <- plot_spectrum(type_occurrences, CT=TRUE, legend=TRUE) # total
        grid.arrange(p2, p3, ncol=2, widths=c(4,2.3), heights=c(4,1))
    }
    plot_96_profile(mut_mat, condensed = TRUE)
    dev.off()
}

###### Section 2: De novo mutational signature extraction using NMF #######
if (!is.na(opt$output_denovo)[1]) {
    mut_mat <- mut_mat + 0.0001 # First add a small psuedocount to the mutation count matrix
    # Use the NMF package to generate an estimate rank plot
    library("NMF")
    estimate <- nmf(mut_mat, rank=1:opt$rank+1, method="brunet", nrun=opt$nrun, seed=123456)
    # And plot it
    pdf(opt$output_denovo, paper = "special", width = 11.69, height = 11.69)
    p.trans <- plot(estimate)
    grid.arrange(p.trans)
    # Extract 4 (PARAMETIZE) mutational signatures from the mutation count matrix with extract_signatures
    # (For larger datasets it is wise to perform more iterations by changing the nrun parameter
    # to achieve stability and avoid local minima)
    nmf_res <- extract_signatures(mut_mat, rank=opt$rank, nrun=opt$nrun)
    # Assign signature names
    colnames(nmf_res$signatures) <- paste0("NewSig_", 1:opt$rank)
    rownames(nmf_res$contribution) <- paste0("NewSig_", 1:opt$rank)
    # Plot the 96-profile of the signatures:
    p.trans <- plot_96_profile(nmf_res$signatures, condensed = TRUE)
    grid.arrange(p.trans)
    # Visualize the contribution of the signatures in a barplot
    pc1 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode="relative", coord_flip = TRUE)
    # Visualize the contribution of the signatures in absolute number of mutations
    pc2 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode="absolute", coord_flip = TRUE)
    # Combine the two plots:

    # plot_contribution_heatmap, which might be easier to interpret and compare than stacked barplots.
    # The samples can be hierarchically clustered based on their euclidean dis- tance. The signatures
    # can be plotted in a user-specified order.
    # Plot signature contribution as a heatmap with sample clustering dendrogram and a specified signature order:
    pch1 <- plot_contribution_heatmap(nmf_res$contribution,
                                      sig_order = paste0("NewSig_", 1:opt$rank))
    # Plot signature contribution as a heatmap without sample clustering:
    pch2 <- plot_contribution_heatmap(nmf_res$contribution, cluster_samples=FALSE)
    #Combine the plots into one figure:
    grid.arrange(pch1, pch2, ncol = 2, widths = c(2,1.6))
    
    # Compare the reconstructed mutational profile with the original mutational profile:   
    plot_compare_profiles(mut_mat[,1],
                          nmf_res$reconstructed[,1],
                          profile_names = c("Original", "Reconstructed"),
                          condensed = TRUE)
    dev.off()
}
##### Section 3: Find optimal contribution of known signatures: COSMIC mutational signatures ####

if (!is.na(opt$output_cosmic)[1]) {
    pdf(opt$output_cosmic, paper = "special", width = 11.69, height = 11.69)
    sp_url <- paste("https://cancer.sanger.ac.uk/cancergenome/assets/", "signatures_probabilities.txt", sep = "")
    cancer_signatures = read.table(sp_url, sep = "\t", header = TRUE)
    mut_mat <- mut_mat + 0.0001 # First add a small psuedocount to the mutation count matrix
    new_order = match(row.names(mut_mat), cancer_signatures$Somatic.Mutation.Type)
    cancer_signatures = cancer_signatures[as.vector(new_order),]
    row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type
    cancer_signatures = as.matrix(cancer_signatures[,4:33])
    
    # Plot mutational profiles of the COSMIC signatures
    colnames(cancer_signatures) <- gsub("Signature.", "", colnames(cancer_signatures)) # shorten signature labels
    p.trans <- plot_96_profile(cancer_signatures, condensed = TRUE, ymax = 0.3)
    grid.arrange(p.trans, top = textGrob("COSMIC signature profiles",gp=gpar(fontsize=12,font=3)))

    # Hierarchically cluster the COSMIC signatures based on their similarity with average linkage
    # hclust_cosmic = cluster_signatures(cancer_signatures, method = "average")
    # store signatures in new order
    # cosmic_order = colnames(cancer_signatures)[hclust_cosmic$order]

    # Plot heatmap with specified signature order
    # p.trans <- plot_cosine_heatmap(cos_sim_samples_signatures, col_order = cosmic_order, cluster_rows = TRUE)
    # grid.arrange(p.trans)
    
    # Find optimal contribution of COSMIC signatures to reconstruct 96 mutational profiles
    fit_res <- fit_to_signatures(mut_mat, cancer_signatures)

    # Select signatures with some contribution (above a threshold)
    threshold <- tail(sort(unlist(rowSums(fit_res$contribution), use.names = FALSE)), opt$signum)[1]
    select <- which(rowSums(fit_res$contribution) >= threshold) # ensure opt$signum best signatures in samples are retained, the others discarded
    # Plot contribution barplots
    pc1 <- plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "absolute")
    pc2 <- plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "relative")
    # Combine the two plots:
    grid.arrange(pc1, pc2)
    ## pie charts of comic signatures contributions in samples
    
    sig_data_pie <- as.data.frame(t(head(fit_res$contribution[select,])))
    colnames(sig_data_pie) <- gsub("nature", "", colnames(sig_data_pie))
    sig_data_pie_percents <- sig_data_pie / (apply(sig_data_pie,1,sum)) * 100
    sig_data_pie_percents$sample <- rownames(sig_data_pie_percents)
    library(reshape2)
    melted_sig_data_pie_percents <-melt(data=sig_data_pie_percents)
    melted_sig_data_pie_percents$label <- sub("Sig.", "", melted_sig_data_pie_percents$variable)
    melted_sig_data_pie_percents$pos <- cumsum(melted_sig_data_pie_percents$value) - melted_sig_data_pie_percents$value/2
    library(ggplot2)
    p.trans <-  ggplot(melted_sig_data_pie_percents, aes(x="", y=value, group=variable, fill=variable)) +
                       geom_bar(width = 1, stat = "identity") +
                       geom_text(aes(label = label), position = position_stack(vjust = 0.5), color="black", size=3) +
                       coord_polar("y", start=0) + facet_wrap(~ sample) +
                       labs(x="", y="Samples", fill = "Signatures (Cosmic_v2,March 2015)") +
                       theme(axis.text = element_blank(),
                             axis.ticks = element_blank(),
                             panel.grid  = element_blank())
    grid.arrange(p.trans)

    # Plot relative contribution of the cancer signatures in each sample as a heatmap with sample clustering
    p.trans <- plot_contribution_heatmap(fit_res$contribution, cluster_samples = TRUE, method = "complete")
    grid.arrange(p.trans)

    # Compare the reconstructed mutational profile of sample 1 with its original mutational profile
    # plot_compare_profiles(mut_mat[,1], fit_res$reconstructed[,1],
    #                       profile_names = c("Original", "Reconstructed"),
    #                       condensed = TRUE)
    
    # Calculate the cosine similarity between all original and reconstructed mutational profiles with
    # `cos_sim_matrix`
    
    # calculate all pairwise cosine similarities
    cos_sim_ori_rec <- cos_sim_matrix(mut_mat, fit_res$reconstructed)
    # extract cosine similarities per sample between original and reconstructed
    cos_sim_ori_rec <- as.data.frame(diag(cos_sim_ori_rec))
    
    # We can use ggplot to make a barplot of the cosine similarities between the original and
    # reconstructed mutational profile of each sample. This clearly shows how well each mutational

    
    # Adjust data frame for plotting with gpplot
    colnames(cos_sim_ori_rec) = "cos_sim"
    cos_sim_ori_rec$sample = row.names(cos_sim_ori_rec)
    # Make barplot
    p.trans <- ggplot(cos_sim_ori_rec, aes(y=cos_sim, x=sample)) +
                      geom_bar(stat="identity", fill = "skyblue4") +
                      coord_cartesian(ylim=c(0.8, 1)) +
                      # coord_flip(ylim=c(0.8,1)) +
                      ylab("Cosine similarity\n original VS reconstructed") +
                      xlab("") +

                      theme_bw() +
                      theme(panel.grid.minor.y=element_blank(),
                      panel.grid.major.y=element_blank()) +
                      # Add cut.off line
                      geom_hline(aes(yintercept=.95))
    grid.arrange(p.trans, top = textGrob("Similarity between true and reconstructed profiles (with all Cosmic sig.)",gp=gpar(fontsize=12,font=3)))    
    
    dev.off()
}

3:e332cf9dfa06

    default = NA,
    type = 'character',
    help = "path to the tab separated file describing the levels in function of datasets"
  ),
  make_option(
    "--cosmic_version",
    default = "v2",
    type = 'character',
    help = "Version of the Cosmic Signature set to be used to express mutational patterns"
  ),
  make_option(
    "--signum",
    default = 2,
    type = 'integer',
    help = "selects the N most significant signatures in samples to express mutational patterns"
  ),

    "--newsignum",
    default = 2,
    type = 'integer',
    help = "Number of new signatures to be captured"
  ),
  make_option(
    "--output_spectrum",
    default = NA,
    type = 'character',
    help = "path to output dataset"

    default = NA,
    type = 'character',
    help = "path to output dataset"
  ),
  make_option(
    "--sigmatrix",
    default = NA,
    type = 'character',
    help = "path to signature matrix"
  ),
  make_option(
    "--output_cosmic",
    default = NA,
    type = 'character',
    help = "path to output dataset"
  ),
  make_option(
    c("-r", "--rdata"),
    type="character",
    default=NULL,
    help="Path to RData output file")
)

opt = parse_args(OptionParser(option_list = option_list),
                 args = commandArgs(trailingOnly = TRUE))

################ Manage input data ####################
json_dict <- opt$inputs
parser <- newJSONParser()
parser$addData(json_dict)
fileslist <- parser$getObject()
vcf_paths <- attr(fileslist, "names")
element_identifiers <- unname(unlist(fileslist))
ref_genome <- opt$genome
vcf_table <- data.frame(element_identifier=element_identifiers, path=vcf_paths)

library(MutationalPatterns)
library(ref_genome, character.only = TRUE)
library(ggplot2)

# Load the VCF files into a GRangesList:
vcfs <- read_vcfs_as_granges(vcf_paths, element_identifiers, ref_genome)
if (!is.na(opt$levels)[1]) {  # manage levels if there are
    levels_table  <- read.delim(opt$levels, header=FALSE, col.names=c("element_identifier","level"))
    library(plyr)
    metadata_table <- join(vcf_table, levels_table, by = "element_identifier")
    tissue <- as.vector(metadata_table$level)
}

##### This is done for any section ######
mut_mat <- mut_matrix(vcf_list = vcfs, ref_genome = ref_genome)

###### Section 1 Mutation characteristics and spectrums #############
if (!is.na(opt$output_spectrum)[1]) {
    pdf(opt$output_spectrum, paper = "special", width = 11.69, height = 11.69)
    type_occurrences <- mut_type_occurrences(vcfs, ref_genome)

    
    if (is.na(opt$levels)[1]) {
        p1 <- plot_spectrum(type_occurrences, CT = TRUE, legend=TRUE)
        plot(p1)
    } else {
        p2 <- plot_spectrum(type_occurrences, by = tissue, CT=TRUE) # by levels
        p3 <- plot_spectrum(type_occurrences, CT=TRUE, legend=TRUE) # total
        grid.arrange(p2, p3, ncol=2, widths=c(4,2.3), heights=c(4,1))
   }
    plot_96_profile(mut_mat, condensed = TRUE)
    dev.off()
}

###### Section 2: De novo mutational signature extraction using NMF #######
if (!is.na(opt$output_denovo)[1]) {
    # opt$rank cannot be higher than the number of samples
    if (opt$rank > length(element_identifiers)) {opt$rank <-length(element_identifiers)}
    # likewise, opt$signum cannot be higher thant the number of samples
    if (opt$signum > length(element_identifiers)) {opt$signum <-length(element_identifiers)}
    pseudo_mut_mat <- mut_mat + 0.0001 # First add a small pseudocount to the mutation count matrix
    # Use the NMF package to generate an estimate rank plot
    library("NMF")
    estimate <- nmf(pseudo_mut_mat, rank=1:opt$rank, method="brunet", nrun=opt$nrun, seed=123456)
    # And plot it
    pdf(opt$output_denovo, paper = "special", width = 11.69, height = 11.69)
    p4 <- plot(estimate)
    grid.arrange(p4)
    # Extract 4 (PARAMETIZE) mutational signatures from the mutation count matrix with extract_signatures
    # (For larger datasets it is wise to perform more iterations by changing the nrun parameter
    # to achieve stability and avoid local minima)
    nmf_res <- extract_signatures(pseudo_mut_mat, rank=opt$newsignum, nrun=opt$nrun)
    # Assign signature names
    colnames(nmf_res$signatures) <- paste0("NewSig_", 1:opt$newsignum)
    rownames(nmf_res$contribution) <- paste0("NewSig_", 1:opt$newsignum)
    # Plot the 96-profile of the signatures:
    p5 <- plot_96_profile(nmf_res$signatures, condensed = TRUE)
    new_sig_matrix <- reshape2::dcast(p5$data, substitution + context ~ variable, value.var = "value")
    new_sig_matrix = format(new_sig_matrix, scientific=TRUE)
    write.table(new_sig_matrix, file=opt$sigmatrix, quote = FALSE, row.names = FALSE, sep="\t") 
    grid.arrange(p5)
    # Visualize the contribution of the signatures in a barplot
    pc1 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode="relative", coord_flip = TRUE)
    # Visualize the contribution of the signatures in absolute number of mutations
    pc2 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode="absolute", coord_flip = TRUE)
    # Combine the two plots:

    # plot_contribution_heatmap, which might be easier to interpret and compare than stacked barplots.
    # The samples can be hierarchically clustered based on their euclidean dis- tance. The signatures
    # can be plotted in a user-specified order.
    # Plot signature contribution as a heatmap with sample clustering dendrogram and a specified signature order:
    pch1 <- plot_contribution_heatmap(nmf_res$contribution,
                                      sig_order = paste0("NewSig_", 1:opt$newsignum))
    # Plot signature contribution as a heatmap without sample clustering:
    pch2 <- plot_contribution_heatmap(nmf_res$contribution, cluster_samples=FALSE)
    #Combine the plots into one figure:
    grid.arrange(pch1, pch2, ncol = 2, widths = c(2,1.6))
    
    # Compare the reconstructed mutational profile with the original mutational profile:   
    plot_compare_profiles(pseudo_mut_mat[,1],
                          nmf_res$reconstructed[,1],
                          profile_names = c("Original", "Reconstructed"),
                          condensed = TRUE)
    dev.off()
}
##### Section 3: Find optimal contribution of known signatures: COSMIC mutational signatures ####

if (!is.na(opt$output_cosmic)[1]) {
    pdf(opt$output_cosmic, paper = "special", width = 11.69, height = 11.69)
    pseudo_mut_mat <- mut_mat + 0.0001 # First add a small psuedocount to the mutation count matrix
    if (opt$cosmic_version == "v2") {
        sp_url <- paste("https://cancer.sanger.ac.uk/cancergenome/assets/", "signatures_probabilities.txt", sep = "")
        cancer_signatures = read.table(sp_url, sep = "\t", header = TRUE)
        new_order = match(row.names(pseudo_mut_mat), cancer_signatures$Somatic.Mutation.Type)
        cancer_signatures = cancer_signatures[as.vector(new_order),]
        row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type
        cancer_signatures = as.matrix(cancer_signatures[,4:33])
        colnames(cancer_signatures) <- gsub("Signature.", "", colnames(cancer_signatures)) # shorten signature labels
        cosmic_tag <- "Signatures (Cosmic v2, March 2015)"
        } else {
        sp_url <- "https://raw.githubusercontent.com/ARTbio/startbio/master/sigProfiler_SBS_signatures_2019_05_22.tsv"
        cancer_signatures = read.table(sp_url, sep = "\t", header = TRUE)
        new_order = match(row.names(pseudo_mut_mat), cancer_signatures$Somatic.Mutation.Type)
        cancer_signatures = cancer_signatures[as.vector(new_order),]
        row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type
        cancer_signatures = as.matrix(cancer_signatures[,4:70])        
        colnames(cancer_signatures) <- gsub("SBS", "", colnames(cancer_signatures)) # shorten signature labels
        cosmic_tag <- "Signatures (Cosmic v3, May 2019)"
        }
    
    # Plot mutational profiles of the COSMIC signatures
    if (opt$cosmic_version == "v2") {
        p6 <- plot_96_profile(cancer_signatures, condensed = TRUE, ymax = 0.3)
        grid.arrange(p6, top = textGrob("COSMIC signature profiles",gp=gpar(fontsize=12,font=3)))
    } else {
        print(length(cancer_signatures))
        p6 <- plot_96_profile(cancer_signatures[,1:33], condensed = TRUE, ymax = 0.3)
        p6bis <- plot_96_profile(cancer_signatures[,34:67], condensed = TRUE, ymax = 0.3)
        grid.arrange(p6, top = textGrob("COSMIC signature profiles (on two pages)",gp=gpar(fontsize=12,font=3)))
        grid.arrange(p6bis, top = textGrob("COSMIC signature profiles (continued)",gp=gpar(fontsize=12,font=3)))
    }

    # Hierarchically cluster the COSMIC signatures based on their similarity with average linkage
    # hclust_cosmic = cluster_signatures(cancer_signatures, method = "average")
    # store signatures in new order
    # cosmic_order = colnames(cancer_signatures)[hclust_cosmic$order]

    # Plot heatmap with specified signature order
    # p.trans <- plot_cosine_heatmap(cos_sim_samples_signatures, col_order = cosmic_order, cluster_rows = TRUE)
    # grid.arrange(p.trans)
    
    # Find optimal contribution of COSMIC signatures to reconstruct 96 mutational profiles
    fit_res <- fit_to_signatures(pseudo_mut_mat, cancer_signatures)

    # Select signatures with some contribution (above a threshold)
    threshold <- tail(sort(unlist(rowSums(fit_res$contribution), use.names = FALSE)), opt$signum)[1]
    select <- which(rowSums(fit_res$contribution) >= threshold) # ensure opt$signum best signatures in samples are retained, the others discarded
    # Plot contribution barplots
    pc3 <- plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "absolute")
    pc4 <- plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "relative")
    #####
    # ggplot2 alternative
    if (!is.na(opt$levels)[1]) {  # if there are levels to display in graphs
        pc3_data <- pc3$data
        pc3_data <- merge (pc3_data, metadata_table[,c(1,3)], by.x="Sample", by.y="element_identifier")
        pc3 <- ggplot(pc3_data, aes(x=Sample, y=Contribution, fill=as.factor(Signature))) +
               geom_bar(stat="identity", position='stack') +
               scale_fill_discrete(name="Cosmic\nSignature") +
               labs(x = "Samples", y = "Absolute contribution") + theme_bw() + 
               theme(panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank()) + 
               facet_grid(~level, scales = "free_x")
        pc4_data <- pc4$data
        pc4_data <- merge (pc4_data, metadata_table[,c(1,3)], by.x="Sample", by.y="element_identifier")
        pc4 <- ggplot(pc4_data, aes(x=Sample, y=Contribution, fill=as.factor(Signature))) +
               geom_bar(stat="identity", position='fill') +
               scale_fill_discrete(name="Cosmic\nSignature") +
               scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
               labs(x = "Samples", y = "Relative contribution") + theme_bw() + 
               theme(panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank()) + 
               facet_grid(~level, scales = "free_x")
    }
    # Combine the two plots:
    grid.arrange(pc3, pc4, top = textGrob("Absolute and Relative Contributions of Cosmic signatures to mutational patterns",gp=gpar(fontsize=12,font=3)))
    ## pie charts of comic signatures contributions in samples
    
    sig_data_pie <- as.data.frame(t(head(fit_res$contribution[select,])))
    colnames(sig_data_pie) <- gsub("nature", "", colnames(sig_data_pie))
    sig_data_pie_percents <- sig_data_pie / (apply(sig_data_pie,1,sum)) * 100
    sig_data_pie_percents$sample <- rownames(sig_data_pie_percents)
    library(reshape2)
    melted_sig_data_pie_percents <-melt(data=sig_data_pie_percents)
    melted_sig_data_pie_percents$label <- sub("Sig.", "", melted_sig_data_pie_percents$variable)
    melted_sig_data_pie_percents$pos <- cumsum(melted_sig_data_pie_percents$value) - melted_sig_data_pie_percents$value/2
    p7 <-  ggplot(melted_sig_data_pie_percents, aes(x="", y=value, group=variable, fill=variable)) +
                       geom_bar(width = 1, stat = "identity") +
                       geom_text(aes(label = label), position = position_stack(vjust = 0.5), color="black", size=3) +
                       coord_polar("y", start=0) + facet_wrap(~ sample) +
                       labs(x="", y="Samples", fill = cosmic_tag) +
                       theme(axis.text = element_blank(),
                             axis.ticks = element_blank(),
                             panel.grid  = element_blank())
    grid.arrange(p7)

    # Plot relative contribution of the cancer signatures in each sample as a heatmap with sample clustering
    p8 <- plot_contribution_heatmap(fit_res$contribution, cluster_samples = TRUE, method = "complete")
    grid.arrange(p8)

    # Compare the reconstructed mutational profile of sample 1 with its original mutational profile
    # plot_compare_profiles(mut_mat[,1], fit_res$reconstructed[,1],
    #                       profile_names = c("Original", "Reconstructed"),
    #                       condensed = TRUE)
    
    # Calculate the cosine similarity between all original and reconstructed mutational profiles with
    # `cos_sim_matrix`
    
    # calculate all pairwise cosine similarities
    cos_sim_ori_rec <- cos_sim_matrix(pseudo_mut_mat, fit_res$reconstructed)
    # extract cosine similarities per sample between original and reconstructed
    cos_sim_ori_rec <- as.data.frame(diag(cos_sim_ori_rec))
    
    # We can use ggplot to make a barplot of the cosine similarities between the original and
    # reconstructed mutational profile of each sample. This clearly shows how well each mutational

    
    # Adjust data frame for plotting with gpplot
    colnames(cos_sim_ori_rec) = "cos_sim"
    cos_sim_ori_rec$sample = row.names(cos_sim_ori_rec)
    # Make barplot
    p9 <- ggplot(cos_sim_ori_rec, aes(y=cos_sim, x=sample)) +
                      geom_bar(stat="identity", fill = "skyblue4") +
                      coord_cartesian(ylim=c(0.8, 1)) +
                      # coord_flip(ylim=c(0.8,1)) +
                      ylab("Cosine similarity\n original VS reconstructed") +
                      xlab("") +

                      theme_bw() +
                      theme(panel.grid.minor.y=element_blank(),
                      panel.grid.major.y=element_blank()) +
                      # Add cut.off line
                      geom_hline(aes(yintercept=.95))
    grid.arrange(p9, top = textGrob("Similarity between true and reconstructed profiles (with all Cosmic sig.)",gp=gpar(fontsize=12,font=3)))    
    dev.off()
}


# Output RData file
if (!is.null(opt$rdata)) {
  save.image(file=opt$rdata)
}