diff mutational_patterns.R @ 0:d585f369bfad draft

"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/mutational_patterns commit 518fb067e8206ecafbf673a5e4cf375ccead11e3"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mutational_patterns.R	Fri Jun 04 22:35:17 2021 +0000
@@ -0,0 +1,422 @@
+# load packages that are provided in the conda env
+options(show.error.messages = F,
+       error = function() {
+           cat(geterrmessage(), file = stderr()); q("no", 1, F)
+           }
+        )
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+warnings()
+library(optparse)
+library(rjson)
+library(grid)
+library(gridExtra)
+library(scales)
+library(RColorBrewer)
+
+# Arguments
+option_list <- list(
+  make_option(
+    "--inputs",
+    default = NA,
+    type = "character",
+    help = "json formatted dictionary of datasets and their paths"
+  ),
+  make_option(
+    "--genome",
+    default = NA,
+    type = "character",
+    help = "genome name in the BSgenome bioconductor package"
+  ),
+  make_option(
+    "--levels",
+    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"
+  ),
+  make_option(
+    "--nrun",
+    default = 2,
+    type = "integer",
+    help = "Number of runs to fit signatures"
+  ),
+  make_option(
+    "--rank",
+    default = 2,
+    type = "integer",
+    help = "number of ranks to display for parameter optimization"
+  ),
+    make_option(
+    "--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"
+  ),
+  make_option(
+    "--output_denovo",
+    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(
+    "--sig_contrib_matrix",
+    default = NA,
+    type = "character",
+    help = "path to signature contribution matrix"
+  ),
+  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 = as.character(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)
+library(plyr)
+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"))
+    } else {
+    levels_table <- data.frame(element_identifier = vcf_table$element_identifier,
+                               level = rep("nolabels", length(vcf_table$element_identifier)))
+}
+metadata_table <- join(vcf_table, levels_table, by = "element_identifier")
+tissue <- as.vector(metadata_table$level)
+detach(package:plyr)
+
+##### This is done for any section ######
+mut_mat <- mut_matrix(vcf_list = vcfs, ref_genome = ref_genome)
+qual_col_pals <- brewer.pal.info[brewer.pal.info$category == "qual", ]
+col_vector <- unique(unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals))))
+col_vector <- col_vector[c(-32, -34, -39)] # 67-color palette
+
+###### Section 1 Mutation characteristics and spectrums #############
+if (!is.na(opt$output_spectrum)[1]) {
+    pdf(opt$output_spectrum, paper = "special", width = 11.69, height = 11.69)
+    type_occurrences <- mut_type_occurrences(vcfs, ref_genome)
+
+    # mutation spectrum, total or by sample
+
+    if (length(levels(factor(levels_table$level))) == 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 #######
+# opt$rank cannot be higher than the number of samples and
+# likewise, opt$signum cannot be higher thant the number of samples
+if (!is.na(opt$output_denovo)[1]) {
+
+    if (opt$rank > length(element_identifiers)) {
+        opt$rank <- length(element_identifiers)
+        }
+    if (opt$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$signature, mode = "absolute", coord_flip = TRUE)
+    # Combine the two plots:
+    grid.arrange(pc1, pc2)
+
+    # The relative contribution of each signature for each sample can also be plotted as a heatmap with
+    # plot_contribution_heatmap, which might be easier to interpret and compare than stacked barplots.
+    # The samples can be hierarchically clustered based on their euclidean dis- tance. The signatures
+    # can be plotted in a user-specified order.
+    # Plot signature contribution as a heatmap with sample clustering dendrogram and a specified signature order:
+    pch1 <- plot_contribution_heatmap(nmf_res$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)"
+        cosmic_colors <- col_vector[1:30]
+        } 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)"
+        cosmic_colors <- col_vector[1:67]
+        }
+
+    # Plot mutational profiles of the COSMIC signatures
+    if (opt$cosmic_version == "v2") {
+        p6 <- plot_96_profile(cancer_signatures, condensed = TRUE, ymax = 0.3)
+        grid.arrange(p6, top = textGrob("COSMIC signature profiles", gp = gpar(fontsize = 12, font = 3)))
+    } else {
+        p6 <- plot_96_profile(cancer_signatures[, 1:33], condensed = TRUE, ymax = 0.3)
+        p6bis <- plot_96_profile(cancer_signatures[, 34:67], condensed = TRUE, ymax = 0.3)
+        grid.arrange(p6, top = textGrob("COSMIC signature profiles (on two pages)",
+                     gp = gpar(fontsize = 12, font = 3)))
+        grid.arrange(p6bis, top = textGrob("COSMIC signature profiles (continued)",
+                     gp = gpar(fontsize = 12, font = 3)))
+    }
+
+
+    # Find optimal contribution of COSMIC signatures to reconstruct 96 mutational profiles
+    fit_res <- fit_to_signatures(pseudo_mut_mat, cancer_signatures)
+
+    # Plot contribution barplots
+        pc3 <- plot_contribution(fit_res$contribution, cancer_signatures, coord_flip = T, mode = "absolute")
+        pc4 <- plot_contribution(fit_res$contribution, cancer_signatures, coord_flip = T, mode = "relative")
+        pc3_data <- pc3$data
+        pc3 <- ggplot(pc3_data, aes(x = Sample, y = Contribution, fill = as.factor(Signature))) +
+               geom_bar(stat = "identity", position = "stack") +
+               coord_flip() +
+               scale_fill_manual(name = "Cosmic\nSignatures", values = cosmic_colors) +
+               labs(x = "Samples", y = "Absolute contribution") + theme_bw() +
+               theme(panel.grid.minor.x = element_blank(),
+                     panel.grid.major.x = element_blank(),
+                     legend.position = "right",
+                     text = element_text(size = 8),
+                     axis.text.x = element_text(angle = 90, hjust = 1))
+        pc4_data <- pc4$data
+        pc4 <- ggplot(pc4_data, aes(x = Sample, y = Contribution, fill = as.factor(Signature))) +
+               geom_bar(stat = "identity", position = "fill") +
+               coord_flip() +
+               scale_fill_manual(name = "Cosmic\nSignatures", values = cosmic_colors) +
+               scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
+               labs(x = "Samples", y = "Relative contribution") + theme_bw() +
+               theme(panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(), legend.position = "right",
+                     text = element_text(size = 8),
+                     axis.text.x = element_text(angle = 90, hjust = 1))
+    #####
+    # ggplot2 alternative
+    if (!is.na(opt$levels)[1]) {  # if there are levels to display in graphs
+        pc3_data <- pc3$data
+        pc3_data <- merge(pc3_data, metadata_table[, c(1, 3)], by.x = "Sample", by.y = "element_identifier")
+        pc3 <- ggplot(pc3_data, aes(x = Sample, y = Contribution, fill = as.factor(Signature))) +
+               geom_bar(stat = "identity", position = "stack") +
+               scale_fill_manual(name = "Cosmic\nSignatures", values = cosmic_colors) +
+               labs(x = "Samples", y = "Absolute contribution") + theme_bw() +
+               theme(panel.grid.minor.x = element_blank(),
+                     panel.grid.major.x = element_blank(),
+                     legend.position = "right",
+                     text = element_text(size = 8),
+                     axis.text.x = element_text(angle = 90, hjust = 1)) +
+               facet_grid(~level, scales = "free_x", space = "free")
+        pc4_data <- pc4$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_manual(name = "Cosmic\nSignatures", values = cosmic_colors) +
+               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(),
+                     legend.position = "right",
+                     text = element_text(size = 8),
+                     axis.text.x = element_text(angle = 90, hjust = 1)) +
+               facet_grid(~level, scales = "free_x", space = "free")
+    }
+    # 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 ###
+    library(reshape2)
+    library(dplyr)
+    if (length(levels(factor(levels_table$level))) < 2) {
+        fit_res_contrib <- as.data.frame(fit_res$contribution)
+        worklist <- cbind(signature = rownames(fit_res$contribution),
+                          level = rep("nolabels", length(fit_res_contrib[, 1])),
+                          fit_res_contrib,
+                          sum = rowSums(fit_res_contrib))
+        worklist <- worklist[order(worklist[, "sum"], decreasing = T), ]
+        worklist <- worklist[1:opt$signum, ]
+        worklist <- worklist[, -length(worklist[1, ])]
+        worklist <- melt(worklist)
+        worklist <- worklist[, c(1, 3, 4, 2)]
+    } else {
+        worklist <- list()
+        for (i in levels(factor(levels_table$level))) {
+             fit_res$contribution[, levels_table$element_identifier[levels_table$level == i]] -> worklist[[i]]
+             sum <- rowSums(as.data.frame(worklist[[i]]))
+             worklist[[i]] <- cbind(worklist[[i]], sum)
+             worklist[[i]] <- worklist[[i]][order(worklist[[i]][, "sum"], decreasing = T), ]
+             worklist[[i]] <- worklist[[i]][1:opt$signum, ]
+             worklist[[i]] <- worklist[[i]][, -length(as.data.frame(worklist[[i]]))]
+        }
+        worklist <- as.data.frame(melt(worklist))
+        worklist[, 2] <- paste0(worklist[, 4], " - ", worklist[, 2])
+        head(worklist)
+    }
+
+    colnames(worklist) <- c("signature", "sample", "value", "level")
+    worklist <- as.data.frame(worklist %>% group_by(sample) %>% mutate(value = value / sum(value) * 100))
+    worklist$pos <- cumsum(worklist$value) - worklist$value / 2
+    worklist$label <- factor(worklist$signature)
+    worklist$signature <- factor(worklist$signature)
+    p7 <-  ggplot(worklist, aes(x = "", y = value, group = signature, fill = signature)) +
+              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) +
+              scale_fill_manual(name = paste0(opt$signum, " most contributing\nsignatures\n(in each label/tissue)"),
+                                values = cosmic_colors[as.numeric(levels(factor(worklist$label)))]) +
+              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
+    if (length(vcf_paths) > 1) {
+        p8 <- plot_contribution_heatmap(fit_res$contribution, cluster_samples = TRUE, method = "complete")
+        grid.arrange(p8)
+    }
+
+    # export relative contribution matrix
+    if (!is.na(opt$sig_contrib_matrix)) {
+        output_table <- t(fit_res$contribution) / rowSums(t(fit_res$contribution))
+        colnames(output_table) <- paste0("s", colnames(output_table))
+        if (length(levels(factor(levels_table$level))) > 1) {
+            output_table <- data.frame(sample = paste0(metadata_table[metadata_table$element_identifier == colnames(fit_res$contribution),
+                                                                    3], "-", colnames(fit_res$contribution)),
+                                       output_table)
+            } else {
+        output_table <- data.frame(sample = rownames(output_table), output_table)
+        }
+        write.table(output_table, file = opt$sig_contrib_matrix, sep = "\t", quote = F, row.names = F)
+    }
+
+    # calculate all pairwise cosine similarities
+    cos_sim_ori_rec <- cos_sim_matrix(pseudo_mut_mat, fit_res$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
+    # profile can be reconstructed with the COSMIC mutational signatures. Two identical profiles
+    # have a cosine similarity of 1. The lower the cosine similarity between original and
+    # reconstructed, the less well the original mutational profile can be reconstructed with
+    # the COSMIC signatures. You could use, for example, cosine similarity of 0.95 as a cutoff.
+
+    # Adjust data frame for plotting with gpplot
+    colnames(cos_sim_ori_rec) <- "cos_sim"
+    cos_sim_ori_rec$sample <- row.names(cos_sim_ori_rec)
+    # Make barplot
+    p9 <- ggplot(cos_sim_ori_rec, aes(y = cos_sim, x = sample)) +
+                      geom_bar(stat = "identity", fill = "skyblue4") +
+                      coord_cartesian(ylim = c(0.8, 1)) +
+                      # coord_flip(ylim=c(0.8,1)) +
+                      ylab("Cosine similarity\n original VS reconstructed") +
+                      xlab("") +
+                      # Reverse order of the samples such that first is up
+                      # xlim(rev(levels(factor(cos_sim_ori_rec$sample)))) +
+                      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)
+}