diff mutational_patterns.R @ 0:924c527fb379 draft

"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/mutational_patterns commit e1f3ca871f13569401f41a5af9d0e281bf372540"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mutational_patterns.R	Sun Sep 13 18:40:29 2020 +0000
@@ -0,0 +1,126 @@
+# 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(MutationalPatterns)
+library(ggplot2)
+
+# 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(
+    "--signum",
+    default = 2,
+    type = 'integer',
+    help = "selects the N most significant signatures in samples to express mutational patterns"
+  ),
+  make_option(
+    "--output",
+    default = NA,
+    type = 'character',
+    help = "path to output dataset"
+  )
+)
+
+opt = parse_args(OptionParser(option_list = option_list),
+                 args = commandArgs(trailingOnly = TRUE))
+
+json_dict <- opt$inputs
+parser <- newJSONParser()
+parser$addData(json_dict)
+fileslist <- parser$getObject()
+vcf_files <- attr(fileslist, "names")
+sample_names <- unname(unlist(fileslist))
+pdf(opt$output, paper = "special", width = 11.69, height = 11.69)
+ref_genome <- opt$genome
+library(ref_genome, character.only = TRUE)
+
+# Load the VCF files into a GRangesList:
+vcfs <- read_vcfs_as_granges(vcf_files, sample_names, ref_genome)
+levels_table  <- read.delim(opt$levels, header=FALSE, col.names=c("sample_name","level"))
+vcf_table <- data.frame(path=vcf_files, sample_name=sample_names)
+metadata_table <- merge(vcf_table, levels_table, by.x=2, by.y=1)
+levels <- metadata_table$level
+muts = mutations_from_vcf(vcfs[[1]])
+types = mut_type(vcfs[[1]])
+context = mut_context(vcfs[[1]], ref_genome)
+type_context = type_context(vcfs[[1]], ref_genome)
+type_occurrences <- mut_type_occurrences(vcfs, ref_genome)
+# p1 <- plot_spectrum(type_occurrences)
+# p2 <- plot_spectrum(type_occurrences, CT = TRUE)
+# p3 <- plot_spectrum(type_occurrences, CT = TRUE, legend = FALSE)
+# 
+# plot(p2)
+# p4 <- plot_spectrum(type_occurrences, by = levels, CT = TRUE, legend = TRUE)
+# palette <- c("pink", "orange", "blue", "lightblue", "green", "red", "purple")
+# p5 <- plot_spectrum(type_occurrences, CT=TRUE, legend=TRUE, colors=palette)
+# 
+# plot(p4)
+mut_mat <- mut_matrix(vcf_list = vcfs, ref_genome = ref_genome)
+# plot_96_profile(mut_mat[,1:length(as.data.frame(mut_mat))], condensed = TRUE)
+mut_mat <- mut_mat + 0.0001
+# library("NMF")
+# estimate <- nmf(mut_mat, rank=2:5, method="brunet", nrun=100, seed=123456)
+# plot(estimate)
+# nmf_res <- extract_signatures(mut_mat, rank = 4, nrun = 100)
+# colnames(nmf_res$signatures) <- c("Signature A", "Signature B", "Signature C", "Signature D")
+# rownames(nmf_res$contribution) <- c("Signature A", "Signature B", "Signature C", "Signature D")
+# plot_96_profile(nmf_res$signatures, condensed = TRUE)
+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(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_96_profile(cancer_signatures, condensed = TRUE, ymax = 0.3)
+hclust_cosmic = cluster_signatures(cancer_signatures, method = "average")
+cosmic_order = colnames(cancer_signatures)[hclust_cosmic$order]
+# plot(hclust_cosmic)
+cos_sim(mut_mat[,1], cancer_signatures[,1])
+cos_sim_samples_signatures = cos_sim_matrix(mut_mat, cancer_signatures)
+plot_cosine_heatmap(cos_sim_samples_signatures, col_order = cosmic_order, cluster_rows = TRUE)
+fit_res <- fit_to_signatures(mut_mat, cancer_signatures)
+threshold <- tail(sort(unlist(rowSums(fit_res$contribution), use.names = FALSE)), opt$signum)[1]
+select <- which(rowSums(fit_res$contribution) >= threshold) # ensure opt$signum best signatures in samples are retained, the others discarded
+plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "absolute")
+plot_contribution(fit_res$contribution[select,], cancer_signatures[,select], coord_flip = T, mode = "relative")
+plot_contribution_heatmap(fit_res$contribution, cluster_samples = TRUE, method = "complete")
+
+
+sig5data <- as.data.frame(t(head(fit_res$contribution[select,])))
+colnames(sig5data) <- gsub("nature", "", colnames(sig5data))
+sig5data_percents <- sig5data / (apply(sig5data,1,sum)) * 100
+sig5data_percents$sample <- rownames(sig5data_percents)
+library(reshape2)
+melted_sig5data_percents <-melt(data=sig5data_percents)
+melted_sig5data_percents$label <- sub("Sig.", "", melted_sig5data_percents$variable)
+melted_sig5data_percents$pos <- cumsum(melted_sig5data_percents$value) - melted_sig5data_percents$value/2
+ggplot(melted_sig5data_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())
+dev.off()