## ----setup, include = FALSE------------------------------------------------ knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE ) suppressPackageStartupMessages(library(metagenomeFeatures)) suppressPackageStartupMessages(library(phyloseq)) ## ----message = FALSE------------------------------------------------------- library(metagenomeFeatures) gg85 <- get_gg13.8_85MgDb() ## -------------------------------------------------------------------------- data(qiita_study_94_gg_ids) ## -------------------------------------------------------------------------- soil_mgF <- annotateFeatures(gg85, qiita_study_94_gg_ids) ## Taxonomic heirarchy soil_mgF # Sequence data mgF_seq(soil_mgF) # Tree data mgF_tree(soil_mgF) ## -------------------------------------------------------------------------- data_dir <- system.file("extdata", package = "metagenomeFeatures") ## Load Biom biom_file <- file.path(data_dir, "229_otu_table.biom") soil_ps <- phyloseq::import_biom(BIOMfilename = biom_file) ## Define sample data sample_file <- file.path(data_dir, "229_sample_data.tsv") sample_dat <- read.delim(sample_file) ## Rownames matching sample_names(), required for phyloseq sample_data slot rownames(sample_dat) <- sample_dat$SampleID sample_data(soil_ps) <- sample_dat ## Resulting phyloseq object soil_ps ## -------------------------------------------------------------------------- # Removing OTUs not in `gg85` soil_tree <- mgF_tree(soil_mgF) soil_ps_gg85 <- prune_taxa(taxa = soil_tree$tip.label, x = soil_ps) # Removing samples with no OTUs in `gg85` samples_to_keep <- sample_sums(soil_ps_gg85) != 0 soil_ps_gg85 <- prune_samples(samples = samples_to_keep, x = soil_ps_gg85) ## -------------------------------------------------------------------------- ## Defining tree slot phy_tree(physeq = soil_ps_gg85) <- soil_tree ## Defining seq slot soil_ps_gg85@refseq <- mgF_seq(soil_mgF) ## ----betaFig, fig.cap="Beta diversity and ordination for a subset of features from Rousk et al. [-@rousk2010soil]. Beta diversity was estimated using Weighted Unifrac and Principal Component Analysis was used for ordination. Sampels are represented as individual point and color indicates soil sample pH."---- soil_ord <- ordinate(physeq = soil_ps_gg85, distance = "wunifrac", method = "PCoA") plot_ordination(soil_ps_gg85, soil_ord, color = "ph", type="sample", label = "SampleID") ## ----sessionInfo, echo=FALSE----------------------------------------------- sessionInfo()