--- a/mutational_patterns.R	Thu Oct 22 23:29:28 2020 +0000
+++ b/mutational_patterns.R	Fri Jun 04 22:35:48 2021 +0000
@@ -1,6 +1,9 @@
 # load packages that are provided in the conda env
-options( show.error.messages=F,
-       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+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)
@@ -11,93 +14,93 @@
 library(RColorBrewer)
 
 # Arguments
-option_list = list(
+option_list <- list(
   make_option(
     "--inputs",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "json formatted dictionary of datasets and their paths"
   ),
   make_option(
     "--genome",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "genome name in the BSgenome bioconductor package"
   ),
   make_option(
     "--levels",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to the tab separated file describing the levels in function of datasets"
   ),
   make_option(
     "--cosmic_version",
     default = "v2",
-    type = 'character',
+    type = "character",
     help = "Version of the Cosmic Signature set to be used to express mutational patterns"
   ),
   make_option(
     "--signum",
     default = 2,
-    type = 'integer',
+    type = "integer",
     help = "selects the N most significant signatures in samples to express mutational patterns"
   ),
   make_option(
     "--nrun",
     default = 2,
-    type = 'integer',
+    type = "integer",
     help = "Number of runs to fit signatures"
   ),
   make_option(
     "--rank",
     default = 2,
-    type = 'integer',
+    type = "integer",
     help = "number of ranks to display for parameter optimization"
   ),
     make_option(
     "--newsignum",
     default = 2,
-    type = 'integer',
+    type = "integer",
     help = "Number of new signatures to be captured"
   ),
   make_option(
     "--output_spectrum",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to output dataset"
   ),
   make_option(
     "--output_denovo",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to output dataset"
   ),
   make_option(
     "--sigmatrix",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to signature matrix"
   ),
   make_option(
     "--output_cosmic",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to output dataset"
   ),
   make_option(
     "--sig_contrib_matrix",
     default = NA,
-    type = 'character',
+    type = "character",
     help = "path to signature contribution matrix"
   ),
   make_option(
     c("-r", "--rdata"),
-    type="character",
-    default=NULL,
-    help="Path to RData output file")
+    type = "character",
+    default = NULL,
+    help = "Path to RData output file")
 )
 
-opt = parse_args(OptionParser(option_list = option_list),
+opt <- parse_args(OptionParser(option_list = option_list),
                  args = commandArgs(trailingOnly = TRUE))
 
 ################ Manage input data ####################
@@ -108,7 +111,7 @@
 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)
+vcf_table <- data.frame(element_identifier = as.character(element_identifiers), path = vcf_paths)
 
 library(MutationalPatterns)
 library(ref_genome, character.only = TRUE)
@@ -118,10 +121,11 @@
 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"))
+    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$element_identifier)))
+    levels_table <- data.frame(element_identifier = vcf_table$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)
@@ -129,39 +133,44 @@
 
 ##### 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))))
+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$level))) == 1) { # (is.na(opt$levels)[1]) 
-        p1 <- plot_spectrum(type_occurrences, CT = TRUE, legend=TRUE)
+
+    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))
+        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$output_denovo)[1]) {
-    # opt$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$signum <-length(element_identifiers)}
+
+    if (opt$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$nrun, seed=123456)
+    estimate <- nmf(pseudo_mut_mat, rank = 1:opt$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)
@@ -169,23 +178,23 @@
     # 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)
+    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$sigmatrix, quote = FALSE, row.names = FALSE, sep="\t") 
+    new_sig_matrix <- format(new_sig_matrix, scientific = TRUE)
+    write.table(new_sig_matrix, file = opt$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)
+    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)
+    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
@@ -194,17 +203,17 @@
     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$contribution, cluster_samples=FALSE)
+    pch2 <- plot_contribution_heatmap(nmf_res$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$reconstructed[,1],
+    grid.arrange(pch1, pch2, ncol = 2, widths = c(2, 1.6))
+
+    # Compare the reconstructed mutational profile with the original mutational profile:
+    plot_compare_profiles(pseudo_mut_mat[, 1],
+                          nmf_res$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$output_cosmic)[1]) {
@@ -212,38 +221,40 @@
     pseudo_mut_mat <- mut_mat + 0.0001 # First add a small psuedocount to the mutation count matrix
     if (opt$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])
+        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])        
+        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)))
+        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)))
+        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)
 
@@ -251,91 +262,99 @@
         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$data
-        pc3 <- ggplot(pc3_data, aes(x=Sample, y=Contribution, fill=as.factor(Signature))) +
-               geom_bar(stat="identity", position='stack') +
+        pc3 <- ggplot(pc3_data, aes(x = Sample, y = Contribution, fill = as.factor(Signature))) +
+               geom_bar(stat = "identity", position = "stack") +
                coord_flip() +
                scale_fill_manual(name = "Cosmic\nSignatures", values = cosmic_colors) +
-               labs(x = "Samples", y = "Absolute contribution") + theme_bw() + 
-               theme(panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(), legend.position="right",
-                     text = element_text(size=8),
-                     axis.text.x = element_text(angle=90, hjust=1))
+               labs(x = "Samples", y = "Absolute contribution") + theme_bw() +
+               theme(panel.grid.minor.x = element_blank(),
+                     panel.grid.major.x = element_blank(),
+                     legend.position = "right",
+                     text = element_text(size = 8),
+                     axis.text.x = element_text(angle = 90, hjust = 1))
         pc4_data <- pc4$data
-        pc4 <- ggplot(pc4_data, aes(x=Sample, y=Contribution, fill=as.factor(Signature))) +
-               geom_bar(stat="identity", position='fill') +
+        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))
+               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') +
+        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")
+               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') +
+        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")
+               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)))
-    
+    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])),
+        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,])]
+                          sum = rowSums(fit_res_contrib))
+        worklist <- worklist[order(worklist[, "sum"], decreasing = T), ]
+        worklist <- worklist[1:opt$signum, ]
+        worklist <- worklist[, -length(worklist[1, ])]
         worklist <- melt(worklist)
-        worklist <- worklist[,c(1,3,4,2)]
+        worklist <- worklist[, c(1, 3, 4, 2)]
     } else {
         worklist <- list()
         for (i in levels(factor(levels_table$level))) {
-             fit_res$contribution[,levels_table$element_identifier[levels_table$level == i]] -> worklist[[i]]
+             fit_res$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$signum,]
-             worklist[[i]] <- worklist[[i]][ , -length(as.data.frame(worklist[[i]]))]
+             worklist[[i]] <- worklist[[i]][order(worklist[[i]][, "sum"], decreasing = T), ]
+             worklist[[i]] <- worklist[[i]][1:opt$signum, ]
+             worklist[[i]] <- worklist[[i]][, -length(as.data.frame(worklist[[i]]))]
         }
         worklist <- as.data.frame(melt(worklist))
-        worklist[,2] <- paste0(worklist[,4], " - ", worklist[,2])
+        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$value/2
+    worklist <- as.data.frame(worklist %>% group_by(sample) %>% mutate(value = value / sum(value) * 100))
+    worklist$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)) +
+    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) +
+              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(),
@@ -348,17 +367,17 @@
         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))
+        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 <- data.frame(sample = paste0(metadata_table[metadata_table$element_identifier == colnames(fit_res$contribution),
+                                                                    3], "-", colnames(fit_res$contribution)),
                                        output_table)
             } else {
-        output_table <- data.frame(sample=rownames(output_table), output_table)
+        output_table <- data.frame(sample = rownames(output_table), output_table)
         }
         write.table(output_table, file = opt$sig_contrib_matrix, sep = "\t", quote = F, row.names = F)
     }
@@ -367,37 +386,37 @@
     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)
+    colnames(cos_sim_ori_rec) <- "cos_sim"
+    cos_sim_ori_rec$sample <- row.names(cos_sim_ori_rec)
     # Make barplot
-    p9 <- ggplot(cos_sim_ori_rec, aes(y=cos_sim, x=sample)) +
-                      geom_bar(stat="identity", fill = "skyblue4") +
-                      coord_cartesian(ylim=c(0.8, 1)) +
+    p9 <- ggplot(cos_sim_ori_rec, aes(y = cos_sim, x = sample)) +
+                      geom_bar(stat = "identity", fill = "skyblue4") +
+                      coord_cartesian(ylim = c(0.8, 1)) +
                       # coord_flip(ylim=c(0.8,1)) +
                       ylab("Cosine similarity\n original VS reconstructed") +
                       xlab("") +
                       # Reverse order of the samples such that first is up
                       # xlim(rev(levels(factor(cos_sim_ori_rec$sample)))) +
                       theme_bw() +
-                      theme(panel.grid.minor.y=element_blank(),
-                      panel.grid.major.y=element_blank()) +
+                      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)))    
+                      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)
+  save.image(file = opt$rdata)
 }

--- a/mutational_patterns.xml	Thu Oct 22 23:29:28 2020 +0000
+++ b/mutational_patterns.xml	Fri Jun 04 22:35:48 2021 +0000
@@ -1,4 +1,4 @@
-<tool id="mutational_patterns" name="Analyse Mutational Patterns/Signatures" version="2.0.0+galaxy13.1">
+<tool id="mutational_patterns" name="Analyse Mutational Patterns/Signatures" version="2.0.0+galaxy14">
     <description>from genomic variations in vcf files</description>
     <requirements>
         <requirement type="package" version="2.0.0=r40_0">bioconductor-mutationalpatterns</requirement>
@@ -6,19 +6,23 @@
         <requirement type="package" version="0.2.20=r40h0357c0b_1002">r-rjson</requirement>
         <requirement type="package" version="0.21.0=r40h0357c0b_1004">r-nmf</requirement>
         <requirement type="package" version="2.3=r40h6115d3f_1003">r-gridextra</requirement>
+        <requirement type="package" version="1.4.3=r40_0">bioconductor-bsgenome.hsapiens.ucsc.hg19</requirement>
         <requirement type="package" version="1.4.3=r40_0">bioconductor-bsgenome.hsapiens.ucsc.hg38</requirement>
-        <requirement type="package" version="0.99.1=r40_4">bioconductor-bsgenome.hsapiens.1000genomes.hs37d5</requirement>
-        <requirement type="package" version="1.4.3=r40_0">bioconductor-bsgenome.hsapiens.ucsc.hg19</requirement>
+<!--
         <requirement type="package" version="1.3.1000=r40_4">bioconductor-bsgenome.hsapiens.ncbi.grch38</requirement>
-        <!-- install bioconda genomes
-        bioconductor-bsgenome.mmusculus.ucsc.mm9
-        bioconductor-bsgenome.mmusculus.ucsc.mm10   -->                    
+        <requirement type="package" version="0.99.1=r40_4">bioconductor-bsgenome.hsapiens.1000genomes.hs37d5</requirement>
+-->
+<!--
+install more bioconda genomes
+bioconductor-bsgenome.mmusculus.ucsc.mm9
+bioconductor-bsgenome.mmusculus.ucsc.mm10
+-->
     </requirements>
     <stdio>
         <exit_code range="1:" level="fatal" description="Tool exception" />
     </stdio>
 
-    <command detect_errors="exit_code"><![CDATA[ 
+    <command detect_errors="exit_code"><![CDATA[
 #import json
 #import os
 Rscript $__tool_directory__/mutational_patterns.R 
@@ -63,8 +67,8 @@
     <inputs>
         <param name="vcfs" type="data_collection" format="vcf" label="VCF file(s) collection" multiple="true"/>
         <param name="genome" type="select" label="Reference Genome">
-            <option value="BSgenome.Hsapiens.1000genomes.hs37d5">BSgenome.Hsapiens.1000genomes.hs37d5</option>
-            <option value="BSgenome.Hsapiens.NCBI.GRCh38">BSgenome.Hsapiens.NCBI.GRCh38</option>
+            <!-- <option value="BSgenome.Hsapiens.1000genomes.hs37d5">BSgenome.Hsapiens.1000genomes.hs37d5</option> -->
+            <!-- <option value="BSgenome.Hsapiens.NCBI.GRCh38">BSgenome.Hsapiens.NCBI.GRCh38</option> -->
             <option value="BSgenome.Hsapiens.UCSC.hg19">BSgenome.Hsapiens.UCSC.hg19</option>
             <option value="BSgenome.Hsapiens.UCSC.hg38" selected="true">BSgenome.Hsapiens.UCSC.hg38</option>
             <!--<option value="BSgenome.Mmusculus.UCSC.mm10">BSgenome.Mmusculus.UCSC.mm10</option>
@@ -280,7 +284,7 @@
 * the absolute contribution of the n most contributing cosmic_ signatures in the samples mutational patterns (to be set by the user, between 2 and 30)
 * the relative contribution of the n most contributing cosmic_ signatures in the samples mutational patterns  (to be set by the user, between 2 and 30)
 * a clustering of the samples with respect to the relative contribution of their cosmic_ signatures
-* pie charts of the samples displaying for each sample the relative contribution of the n most contributing cosmic_ signatures in their mutational pattern
+* pie charts of the samples displaying for each sample the relative contribution of the n most contributing cosmic_ signatures to their mutational pattern
 
 .. _cosmic: https://cancer.sanger.ac.uk/cosmic/signatures_v2.tt