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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/phyloseq commit 5ec9f9e81bb9a42dec5c331dd23215ca0b027b2b
author | iuc |
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date | Sat, 16 Mar 2024 07:55:48 +0000 |
parents | b85ba18a8f36 |
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#!/usr/bin/env Rscript suppressPackageStartupMessages(library("optparse")) suppressPackageStartupMessages(library("phyloseq")) suppressPackageStartupMessages(library("tidyverse")) option_list <- list( make_option(c("--sequence_table"), action = "store", dest = "sequence_table", help = "Input sequence table"), make_option(c("--taxonomy_table"), action = "store", dest = "taxonomy_table", help = "Input taxonomy table"), make_option(c("--sample_table"), action = "store", default = NULL, dest = "sample_table", help = "Input sample table"), make_option(c("--output"), action = "store", dest = "output", help = "RDS output") ) parser <- OptionParser(usage = "%prog [options] file", option_list = option_list) args <- parse_args(parser, positional_arguments = TRUE) opt <- args$options # The input sequence_table is an integer matrix # stored as tabular (rows = samples, columns = ASVs). seq_table_numeric_matrix <- data.matrix(read.table(opt$sequence_table, header = T, sep = "\t", row.names = 1, check.names = FALSE)) # The input taxonomy_table is a table containing # the assigned taxonomies exceeding the minBoot # level of bootstrapping confidence. Rows correspond # to sequences, columns to taxonomic levels. NA # indicates that the sequence was not consistently # classified at that level at the minBoot threshold. tax_table_matrix <- as.matrix(read.table(opt$taxonomy_table, header = T, sep = "\t", row.names = 1, check.names = FALSE)) # Construct a tax_table object. The rownames of # tax_tab must match the OTU names (taxa_names) # of the otu_table defined below. tax_tab <- tax_table(tax_table_matrix) # Construct an otu_table object. otu_tab <- otu_table(seq_table_numeric_matrix, taxa_are_rows = TRUE) # Construct a phyloseq object. phyloseq_obj <- phyloseq(otu_tab, tax_tab) if (!is.null(opt$sample_table)) { sample_tab <- sample_data( read.table(opt$sample_table, header = T, sep = "\t", row.names = 1, check.names = FALSE) ) phyloseq_obj <- merge_phyloseq(phyloseq_obj, sample_tab) } # use short names for our ASVs and save the ASV sequences # refseq slot of the phyloseq object as described in # https://benjjneb.github.io/dada2/tutorial.html dna <- Biostrings::DNAStringSet(taxa_names(phyloseq_obj)) names(dna) <- taxa_names(phyloseq_obj) phyloseq_obj <- merge_phyloseq(phyloseq_obj, dna) taxa_names(phyloseq_obj) <- paste0("ASV", seq(ntaxa(phyloseq_obj))) print(phyloseq_obj) # save R object to file saveRDS(phyloseq_obj, file = opt$output, compress = TRUE)