diff --git a/.Rhistory b/.Rhistory index 324402eee85dda94e70b6294cff9a362ab436220..1d32261190984fa56c90ac53b3def39fb108b00b 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,24 +1,3 @@ -names(colors_genera_pp) <- genera_pp -colors_genera_other <- viridis(length(genera_other), option = "D", begin = 1 ,end = 0.9) -names(colors_genera_other) <- genera_other -colors_oomyc_genera <- c(colors_genera_pp, colors_genera_other) -#Sort alphabetically by vector names to avoid mixing up color assignments (origin of issue not resolved) -colors_oomyc_genera <- colors_oomyc_genera[order(names(colors_oomyc_genera))] -colors_oomyc_genera["Phytophthora"] <- viridis_reds[1] -colors_oomyc_genera["Pythium"] <- viridis_reds[4] -shared_theme <- theme(legend.key.size = unit(3, "mm"), legend.position = "none", axis.title.x = element_blank(), plot.margin = margin(20,10,10,10), legend.text.align = 0) -shared_theme_abu <- theme(legend.key.size = unit(3, "mm"), legend.position = "none", axis.title.x = element_blank(), plot.margin = margin(20,10,10,10),legend.text.align = 0) -if(customLabels){ -labelling <- c(expression(paste("uncultured ", italic("Apodachlya"))), -expression(paste("uncultured ", italic("Lagenidium"))), -expression(paste("uncultured ", italic("Oomycetes"))), -expression(paste("uncultured ", italic("Phytophthora"))), -expression(paste("uncultured ", italic("Pythium"))), -expression(italic("Achlya"), italic("Albugo"), italic("Aphanomyces"), -italic("Apodachlya"), italic("Atkinsiella"), italic("Bremia"), -italic("Brevilegnia"), italic("Dictyuchus"), italic("Eurychasma"), -italic("Geolegnia"), italic("Globisporangium"), italic("Halocrusticida"), -italic("Haptoglossa"), italic("Hyaloperonospora"), italic("Lagena"), italic("Lagenidium"), italic("Leptolegnia"), italic("Myzocytiopsis"), italic("Paralagenidium"), italic("Peronospora"), italic("Phragmosporangium"), italic("Phytophthora"), italic("Phytopythium"), italic("Pilasporangium"), @@ -510,3 +489,24 @@ cat(topnt_summary, "\n\n", topnt2_summary) cat(prctg_pyth_phyt_str) combobar1 cat("Chunk successfully run") +# CHANGE ME according to the location containing 'seqtab_nochim.rds' and other relevant files. +path = "Data_files/" +# CHANGE ME to 'TRUE' to list all samples and generate an empty metadata file +optional_sample_check <- "FALSE" +# CHANGE ME to TRUE to update the cuphyr package. This is a package the author published to separate out general purpose functions from this script. If the package is not installed, it will be installed, regardless of the value chosen here. +update_cuphyr <- TRUE +################# NO CHANGES NECESSARY BELOW ################# +#global knitr settings to determine the output of the markdown document upon 'knitting' +knitr::opts_chunk$set(echo = TRUE) +knitr::opts_chunk$set(root.dir = paste0(path)) +#Messages and warnings are turned off globally for a cleaner output. Change to TRUE for complete output +knitr::opts_chunk$set(message = FALSE) +knitr::opts_chunk$set(warning = FALSE) +knitr::opts_chunk$set(tidy = TRUE) +#CuPhyR is the author's own package that contains several functions used to process phyloseq objects. It is hosted on GitHub and installed/updated using this command if update_cuphyr is TRUE +if (update_cuphyr | !"cuphyr" %in% rownames(installed.packages())) { +devtools::install_github("simeross/cuphyr") +} +#Loading required packages +library(phyloseq) +library(dada2) diff --git a/Data_files/phyloseq_output/Barplot_controls_combined_supfig2.png b/Data_files/phyloseq_output/Barplot_controls_combined_supfig2_14-10-20_150659.png similarity index 100% rename from Data_files/phyloseq_output/Barplot_controls_combined_supfig2.png rename to Data_files/phyloseq_output/Barplot_controls_combined_supfig2_14-10-20_150659.png diff --git a/Data_files/phyloseq_output/Class_absolute_ranking_Supfig3.png b/Data_files/phyloseq_output/Class_absolute_ranking_Supfig3_14-10-20_151709.png similarity index 100% rename from Data_files/phyloseq_output/Class_absolute_ranking_Supfig3.png rename to Data_files/phyloseq_output/Class_absolute_ranking_Supfig3_14-10-20_151709.png diff --git a/Data_files/phyloseq_output/Class_ranking_Fig3.png b/Data_files/phyloseq_output/Class_ranking_Fig3_14-10-20_151641.png similarity index 100% rename from Data_files/phyloseq_output/Class_ranking_Fig3.png rename to Data_files/phyloseq_output/Class_ranking_Fig3_14-10-20_151641.png diff --git a/Data_files/phyloseq_output/FunGuild_combo_Fig2.png b/Data_files/phyloseq_output/FunGuild_combo_Fig2_14-10-20_151625.png similarity index 100% rename from Data_files/phyloseq_output/FunGuild_combo_Fig2.png rename to Data_files/phyloseq_output/FunGuild_combo_Fig2_14-10-20_151625.png diff --git a/Data_files/phyloseq_output/IsolateSeqs_ASVs_heatmap_Fig4.png b/Data_files/phyloseq_output/IsolateSeqs_ASVs_heatmap_Fig4_14-10-20_151706.png similarity index 100% rename from Data_files/phyloseq_output/IsolateSeqs_ASVs_heatmap_Fig4.png rename to Data_files/phyloseq_output/IsolateSeqs_ASVs_heatmap_Fig4_14-10-20_151706.png diff --git a/Data_files/phyloseq_output/IsolateSeqs_ASVs_ref_tree_Supfig4.png b/Data_files/phyloseq_output/IsolateSeqs_ASVs_ref_tree_Supfig4_14-10-20_151704.png similarity index 100% rename from Data_files/phyloseq_output/IsolateSeqs_ASVs_ref_tree_Supfig4.png rename to Data_files/phyloseq_output/IsolateSeqs_ASVs_ref_tree_Supfig4_14-10-20_151704.png diff --git a/Data_files/phyloseq_output/Overview_SupFig1.png b/Data_files/phyloseq_output/Overview_SupFig1_14-10-20_150812.png similarity index 100% rename from Data_files/phyloseq_output/Overview_SupFig1.png rename to Data_files/phyloseq_output/Overview_SupFig1_14-10-20_150812.png diff --git a/Data_files/phyloseq_output/Pos-ASVs_kontr-seqs_tree.png b/Data_files/phyloseq_output/Pos-ASVs_kontr-seqs_tree_14-10-20_150811.png similarity index 100% rename from Data_files/phyloseq_output/Pos-ASVs_kontr-seqs_tree.png rename to Data_files/phyloseq_output/Pos-ASVs_kontr-seqs_tree_14-10-20_150811.png diff --git a/Data_files/phyloseq_output/Topn_barplot_Fig1.png b/Data_files/phyloseq_output/Topn_barplot_Fig1_14-10-20_150845.png similarity index 100% rename from Data_files/phyloseq_output/Topn_barplot_Fig1.png rename to Data_files/phyloseq_output/Topn_barplot_Fig1_14-10-20_150845.png diff --git a/SupMat3_submission_JoAE.Rmd b/SupMat3_submission_JoAE.Rmd index 1a571c5850ba4a77b0564ecaf52420364ea603db..1377fad329d63d45852d46de667b28eefd32d24f 100644 --- a/SupMat3_submission_JoAE.Rmd +++ b/SupMat3_submission_JoAE.Rmd @@ -13,7 +13,7 @@ urlcolor: blue If all needed packages (see below) are installed and the entire Git repository belonging to the publication was cloned so that the file organization is identical, it should be possible to run and reproduce the entire data analysis using this R Markdown file without any changes or adjustments. -This is an R Markdown file containing code to document the data analysis for the manuscript "DNA metabarcoding reveals broad presence of potentially plant pathogenic oomycetes in cross-border transported soil". Results of the DADA2 analysis are parsed into phyloseq and other, generic data objects and metadata is added. In addition to documenting the analysis, the analysis should be fully reproducible using the provided files at ##GITHUB REPOSITORY##. It is separated into chunks that may be run independently by pressing the _play_ button. To reproduce the reported results, all chunks have to be run from top to bottom. **Several additional files** are required in order to run the entire pipeline successfully: +This is an R Markdown file containing code to document the data analysis for the manuscript "DNA metabarcoding reveals broad presence of potentially plant pathogenic oomycetes in cross-border transported soil". Results of the DADA2 analysis are parsed into phyloseq and other, generic data objects and metadata is added. In addition to documenting the analysis, the analysis should be fully reproducible using the provided files at the [GitLab repository](https://gitlab.nibio.no/simeon/oomycete-metabarcoding-supplementary). It is separated into chunks that may be run independently by pressing the _play_ button. To reproduce the reported results, all chunks have to be run from top to bottom. **Several additional files** are required in order to run the entire pipeline successfully: * A sequence table called **'seqtab_nochim.rds'** (automatically generated by the DADA2 pipeline) * A taxonomy table called **'taxa.rds'** (automatically generated by the Dada2 pipeline) @@ -33,29 +33,40 @@ The file 'descriptors.txt' is a tab-delimited .txt table containing metadata for Warnings are disabled for this chunk as at least one function (dir.create) throws an expected warning every time the output path already exists. Set 'warning=TRUE' in the chunk header if you want to output warnings to the knitted document and output space under a given chunk (e.g. if you experience trouble loading certain libraries). ```{r check samples, message=FALSE, tidy=FALSE, warning=FALSE} -# CHANGE ME according to the location containing 'seqtab_nochim.rds' and other relevant files. +# CHANGE ME according to the location containing 'seqtab_nochim.rds' and other +# relevant files. path = "Data_files/" # CHANGE ME to 'TRUE' to list all samples and generate an empty metadata file optional_sample_check <- "FALSE" -# CHANGE ME to TRUE to update the cuphyr package. This is a package the author published to separate out general purpose functions from this script. If the package is not installed, it will be installed, regardless of the value chosen here. +# CHANGE ME to TRUE to update the cuphyr package. This is a package the author +# published to separate out general purpose functions from this script. If the +# package is not installed, it will be installed, regardless of the value chosen +# here. update_cuphyr <- TRUE -################# NO CHANGES NECESSARY BELOW ################# -#global knitr settings to determine the output of the markdown document upon 'knitting' -knitr::opts_chunk$set(echo = FALSE) +################# NO CHANGES NECESSARY BELOW ################# + +# global knitr settings to determine the output of the markdown document upon +# 'knitting' +knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(root.dir = paste0(path)) -#Messages and warnings are turned off globally for a cleaner output. Change to TRUE for complete output + +# Messages and warnings are turned off globally for a cleaner output. +# Change to TRUE for complete output knitr::opts_chunk$set(message = FALSE) knitr::opts_chunk$set(warning = FALSE) +knitr::opts_chunk$set(tidy = FALSE) -#CuPhyR is the author's own package that contains several functions used to process phyloseq objects. It is hosted on GitHub and installed/updated using this command if update_cuphyr is TRUE +# CuPhyR is the author's own package that contains several functions used to +# process phyloseq objects. It is hosted on GitHub and installed/updated using +# this command if update_cuphyr is TRUE if (update_cuphyr | !"cuphyr" %in% rownames(installed.packages())) { devtools::install_github("simeross/cuphyr") } -#Loading required packages +# Loading required packages library(phyloseq) library(dada2) library(tidyverse) @@ -74,10 +85,11 @@ library(cuphyr) library(data.table) -#Defining output directory, this is where generated plots will be saved to. +# Defining output directory, this is where generated plots will be saved to. outp <- paste0(path,"/phyloseq_output") -#Checks whether output path exists and creates it if not. Throws warning if directory exists. +# Checks whether output path exists and creates it if not. Throws warning if +# directory exists. dir.create(file.path(outp)) if (optional_sample_check == "TRUE") { @@ -85,17 +97,18 @@ if (optional_sample_check == "TRUE") { samps <- rownames(seqtabcheck) lensamps <- length(samps) blankcol <- vector(mode = "character", length = lensamps) - blanktable <- data.frame(SampleIDs = samps, - ExampleProperty1 = blankcol, - ExampleProperty2 = blankcol, - ExampleProperty3 = blankcol) + blanktable <- data.frame(SampleIDs = samps, + ExampleProperty1 = blankcol, + ExampleProperty2 = blankcol, + ExampleProperty3 = blankcol) write.table(blanktable, file = paste0(path, "/descriptors_blank.txt"), sep = "\t", row.names = F) cat("'seqtab_nochim.rds' contains samples in the following order:\n", samps, "\nThe number of samples in the file is:", lensamps,sep = "\n") } -#Setting seed for reproducible results of randomized functions (mostly phylogenetic trees) +# Setting seed for reproducible results of randomized functions (mostly +# phylogenetic trees) set.seed(20301) #minmal message to confirm successful chunk pass @@ -108,28 +121,47 @@ cat("Chunk successfully run") This chunk allows the adjustment of several analysis parameters, such as **setting the pruning of control samples** based on keywords, **requiring that a phylogenetic tree be provided or generated**, **defining a minimum ASV count** and **providing an alternative taxonomy**. ```{r Parameters, tidy=FALSE} -## CHANGE ME to "TRUE" to remove control samples from the analysis or "FALSE" to analyse all samples. +## CHANGE ME to "TRUE" to remove control samples from the analysis or "FALSE" to +# analyse all samples. Prune_Controls <- "TRUE" -## CHANGE ME to a list of unique identifiers that only occur in the names of control samples to classify. These samples will be separated into a dedicated phyloseq object to be analyzed apart from the core samples. Common examples are provided. +## CHANGE ME to a list of unique identifiers that only occur in the names of +# control samples to classify. These samples will be separated into a dedicated +# phyloseq object to be analyzed apart from the core samples. Common examples +# are provided. controls <- c("Pos", "H2O", "Neg", "Kontr", "Contr", "posK", "NTC", "POSK") -## CHANGE ME to a number of ASV counts [~reads] that analyzed samples should minimally have. Samples with lower ASV counts than 'minASVcount' will be pruned. +## CHANGE ME to a number of ASV counts [~reads] that analyzed samples should +# minimally have. Samples with lower ASV counts than 'minASVcount' will be +# pruned. minASVcount <- 5000 -## CHANGE ME to "TRUE", if you want to provide a custom taxonomy table instead of using the default dada2 output ('taxa.rds'). +## CHANGE ME to "TRUE", if you want to provide a custom taxonomy table instead +# of using the default dada2 output ('taxa.rds'). customTax <- "TRUE" - ## CHANGE ME to the location of the custom taxonomy file. This only matters if customTax="TRUE", otherwise it will be ignored. + ## CHANGE ME to the location of the custom taxonomy file. This only matters if + # customTax="TRUE", otherwise it will be ignored. taxfile <- file.path(path, "custom_BLAST_taxonomy_nt.txt") -## CHANGE ME to "TRUE" to generate a phylogenetic tree. This process takes a long time depending on the number of sequences (up to days for thousands). If a tree is provided as 'phylotree.rds' in 'path', then it will be used regardless of the value of 'maketree' +## CHANGE ME to "TRUE" to generate a phylogenetic tree. This process takes a +# long time depending on the number of sequences (up to days for thousands). +# If a tree is provided as 'phylotree.rds' in 'path', then it will be used +# regardless of the value of 'maketree' maketree <- "TRUE" -## CHANGE ME to "TRUE" to root the used phylogenetic tree (if one exists) on the leaf with the longest branch (outgroup). This makes analyses that rely on the phylogenetic tree reproducible instead of picking a random leaf as root when calculating UNIFRAC distances. Implementation based on http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in https://github.com/joey711/phyloseq/issues/597 +## CHANGE ME to "TRUE" to root the used phylogenetic tree (if one exists) on the +# leaf with the longest branch (outgroup). This makes analyses that rely on the +# phylogenetic tree reproducible instead of picking a random leaf as root when +# calculating UNIFRAC distances. Implementation based on +# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in +# https://github.com/joey711/phyloseq/issues/597 roottree <- "TRUE" -#Determines whether a new version of the FUNGuild database should be downloaded and saved in 'path'. Otherwise uses existing RDS file in 'path'. If none exists, a new one will be downloaded regardless of the value of 'update_to_current'. +# Determines whether a new version of the FUNGuild database should be downloaded +# and saved in 'path'. Otherwise uses existing RDS file in 'path'. If none +# exists, a new one will be downloaded regardless of the value of +# 'update_to_current'. update_to_current <- "FALSE" #Setting a seed so functions that use randomization produce reproducible results @@ -157,12 +189,14 @@ if (customTax == "TRUE") { }else{ taxap <- readRDS(file.path(path,"taxa.rds"))} #Read in metadata -samp_table <- read.delim(paste0(path, "/descriptors.txt"), header = TRUE, sep = "\t") +samp_table <- read.delim(paste0(path, "/descriptors.txt"), header = TRUE, + sep = "\t") #List samples from Sequence table samp_list <- rownames(seqtabp) -##generate phylogenetic tree of ASVs only if there is no file called 'phylotree.rds' in the working directory and 'maketree' is "TRUE" +# generate phylogenetic tree of ASVs only if there is no file called +# 'phylotree.rds' in the working directory and 'maketree' is "TRUE" if (!file.exists(paste0(path, "/phylotree.rds"))) { if (maketree == "TRUE") { ASVs <- getSequences(seqtabp) @@ -174,16 +208,21 @@ if (!file.exists(paste0(path, "/phylotree.rds"))) { fit = pml(treeNJ, data = ASV_phang) fitGTR <- update(fit, k = 4, inv = 0.2) fitGTR <- optim.pml(fitGTR, model = "GTR", optInv = TRUE, optGamma = TRUE, - rearrangement = "stochastic", control = pml.control(trace = 0)) + rearrangement = "stochastic", + control = pml.control(trace = 0)) saveRDS(fitGTR, file = paste0(path, "/phylotree.rds"))}} -##parse into phyloseq object, either with or without phylogenetic tree information +## parse into phyloseq object, either with or without phylogenetic tree +# information row.names(samp_table) <- samp_list if (file.exists(paste0(path, "/phylotree.rds"))) { treep <- readRDS(paste0(path, "/phylotree.rds")) - p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), sample_data(samp_table), tax_table(taxap), phy_tree(treep$tree)) + p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), + sample_data(samp_table), tax_table(taxap), + phy_tree(treep$tree)) }else{ - p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), sample_data(samp_table), tax_table(taxap))} + p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), + sample_data(samp_table), tax_table(taxap))} ##Adding nucleotide info and giving sequences ASV## identifiers dna <- Biostrings::DNAStringSet(taxa_names(p)) @@ -194,26 +233,34 @@ taxa_names(p) <- paste0("ASV", seq(ntaxa(p))) ##optional pruning if (Prune_Controls == "TRUE") { if (!is.null(controls)) { - samp_clean <- samp_list[!samp_list %in% grep(paste0(controls, collapse = "|"), samp_list, value = T)] + samp_clean <- samp_list[!samp_list %in% grep(paste0(controls, + collapse = "|"), + samp_list, value = T)] contr_pruned <- setdiff(samp_list, samp_clean) ps <- prune_samples(samp_clean, p) - cat("\n", "Number of control samples that were pruned and will not be analysed:\n", - length(samp_list) - length(samp_clean), "\n", - "The following controls were pruned:\n", contr_pruned, "\n", sep = "\n") - }else{cat("\n\nPrune_Controls is TRUE but 'controls' is empty. No samples were pruned.\n\n")} + cat("\n", + "Number of control samples that were pruned and will not be analysed:\n", + length(samp_list) - length(samp_clean), + "\n", + "The following controls were pruned:\n", + contr_pruned, "\n", sep = "\n") + }else{ + cat("\n\nPrune_Controls is TRUE but 'controls' is empty. No samples were pruned.\n\n")} }else{ps <- p} if (minASVcount > 0) { ps.raw <- ps samp_pruned <- names(which(sample_sums(ps) < minASVcount)) ps <- prune_samples(sample_sums(ps) >= minASVcount, ps) if (length(samp_pruned) > 0) { - cat("The following samples were pruned because ASV counts were lower than", minASVcount, ":\n") + cat("The following samples were pruned because ASV counts were lower than", + minASVcount, ":\n") cat(samp_pruned, "\n", sep = "\n")} } #Remove 0 count ASVs (e.g. control ASVs that remain) from the base object ps <- prune_taxa(taxa_sums(ps) > 0, ps) ps -cat("","Summary of sample counts","Average ASV count per sample (incl. controls and unfiltered):", +cat("","Summary of sample counts", + "Average ASV count per sample (incl. controls and unfiltered):", mean(phyloseq::sample_sums(p)), "Average ASV count per sample after pruning controls and filtering low count samples:", mean(phyloseq::sample_sums(ps)), sep = "\n") @@ -227,7 +274,12 @@ This chunk defines various custom functions that are used in chunks below. **Thi ```{r Functions, tidy=FALSE} ################################################ -#This funtion defines the leaf with the longest path as the root of the phylogenetic tree. This makes results reproducible by avoiding the behaviour of some functions that would otherwise pick a random leaf as the root of an unrooted phylogenetic tree. Based on answers in https://github.com/joey711/phyloseq/issues/597 + +# This funtion defines the leaf with the longest path as the root of the +# phylogenetic tree. This makes results reproducible by avoiding the behaviour +# of some functions that would otherwise pick a random leaf as the root of an +# unrooted phylogenetic tree. Based on answers in +# https://github.com/joey711/phyloseq/issues/597 pick_new_outgroup <- function(tree.unrooted){ require("magrittr") require("data.table") @@ -243,7 +295,9 @@ pick_new_outgroup <- function(tree.unrooted){ return(new.outgroup) } ################################################ -#Generic save function for plots that checks whether file exists and if so, creates a new one with d/m/y+time info to avoid overwriting. + +# Generic save function for plots that checks whether file exists and if so, +# creates a new one with d/m/y+time info to avoid overwriting. save_plot <- function(pl, filetype = ".png", plot_name = "my_plot"){ name <- paste0("/",plot_name,filetype) if (file.exists(paste0(outp, name))) { @@ -252,8 +306,11 @@ ggsave(paste0(outp, name), pl, width = wp, height = hp, unit = "cm", dpi = res) } ################################################ -#Function to download and parse the FUNGuild, adapted from S. Faye Smith (2018) in https://rpubs.com/faysmith/metabarcoding -parse_funguild <- function(url = 'http://www.stbates.org/funguild_db.php', tax_name = TRUE){ + +# Function to download and parse the FUNGuild, adapted from S. Faye Smith (2018) +# in https://rpubs.com/faysmith/metabarcoding +parse_funguild <- function(url = 'http://www.stbates.org/funguild_db.php', + tax_name = TRUE){ require(XML) require(jsonlite) require(RCurl) @@ -266,7 +323,8 @@ parse_funguild <- function(url = 'http://www.stbates.org/funguild_db.php', tax_n db$`_id` <- NULL if (tax_name == TRUE) { ## Code legend - ## Taxon Level: A numeral corresponding the correct taxonomic level for the taxon + ## Taxon Level: A numeral corresponding the correct taxonomic level for the + # taxon taxons <- c( "keyword", # 0 "Phylum", "Subphylum", "Class", "Subclass", "Order", "Suborder", # 3:8 @@ -279,12 +337,9 @@ parse_funguild <- function(url = 'http://www.stbates.org/funguild_db.php', tax_n TaxID = c(0, 3:13, 15:26), Taxon = factor(taxons, levels = taxons)) ## Match taxon codes - db$taxonomicLevel <- taxmatch[match(x = db$taxonomicLevel, table = taxmatch$TaxID), "Taxon"] + db$taxonomicLevel <- taxmatch[match(x = db$taxonomicLevel, + table = taxmatch$TaxID), "Taxon"] } - # remove rows with missing data - # which( - # with(db, trophicMode == "NULL" & guild == "NULL" & growthForm == "NULL" & trait == "NULL" & notes == "NULL") - # ) ## Add database dump date as attributes to the result attr(db, "DownloadDate") <- date() db <- as_tibble(db) @@ -297,20 +352,22 @@ cat("Chunk successfully run") This chunk calculates most phyloseq objects, vectors, lists and tables that are used to generate figures and other output below. This somewhat clutters the global environment but keeps the data available and makes the chunks that generate output more efficient. **This chunk is absolutely necessary for downstream code to work.** -```{r Phyloseq_objects_and_sets} -#Get a tbl of the base object for easier access in some phyloseq-independent analyses +```{r Phyloseq_objects_and_sets, tidy=FALSE} +# Get a tbl of the base object for easier access in some phyloseq-independent +# analyses ps_tbl <- as_tibble(psmelt(ps)) -#Transformed +# Transformed ps.trans <- transform_sample_counts(ps, function(ASV) ASV/sum(ASV)) -#Just controls +# Just controls ps.contr <- subset_samples(p, Comment == "Control") ps.contr <- prune_taxa(taxa_sums(ps.contr) >= 20, ps.contr) ps.transcont <- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV)) -#FUNGuild update check and/or load -if (update_to_current == "TRUE" | !file.exists(paste0(path, "/FUNGuild-db.rds"))) { +# FUNGuild update check and/or load +if (update_to_current == "TRUE" | !file.exists(paste0(path, + "/FUNGuild-db.rds"))) { FUNGuild <- parse_funguild() saveRDS(FUNGuild, file = paste0(path, "/FUNGuild-db.rds")) }else{ @@ -318,29 +375,33 @@ if (update_to_current == "TRUE" | !file.exists(paste0(path, "/FUNGuild-db.rds")) } FUNGuild_tbl <- select(FUNGuild, taxon, trophicMode:growthForm) -#Generate a ps_tbl subset for oomycetes +# Generate a ps_tbl subset for oomycetes ps_tbl_Oomycs <- filter(ps_tbl, str_detect(Class, 'Oomycetes')) %>% select(OTU, Abundance, Kingdom:Species) %>% mutate(Species = gsub("_", " ", Species)) %>% group_by(OTU) -#Make a tbl of all oomycete ASVs and their classificaiton according to FUNGuild +# Make a tbl of all oomycete ASVs and their classificaiton according to FUNGuild ASVs_funguild <- summarise(ps_tbl_Oomycs, Abundance_sum = sum(Abundance)) %>% left_join(ps_tbl_Oomycs, by = "OTU") %>% select(-Abundance) %>% distinct() %>% left_join(FUNGuild_tbl, by = c("Genus" = "taxon")) %>% left_join(FUNGuild_tbl, by = c("Species" = "taxon")) %>% - dplyr::rename(trophic_genus = trophicMode.x, guild_genus = guild.x, - trophic_species = trophicMode.y, guild_species = guild.y) %>% + dplyr::rename(trophic_genus = trophicMode.x, + guild_genus = guild.x, + trophic_species = trophicMode.y, + guild_species = guild.y) %>% arrange(Genus) %>% - unite(Plant_pathogenic, c(guild_genus, guild_species), remove = FALSE) %>% - mutate(Plant_pathogenic = str_replace(Plant_pathogenic, "Plant Pathogen_Plant Pathogen", "++")) %>% - mutate(Plant_pathogenic = str_replace(Plant_pathogenic, "Plant Pathogen_NA", "+")) %>% - mutate(Plant_pathogenic = str_replace(Plant_pathogenic, "NA_NA", "-")) #%>% - #mutate(guild_genus = ifelse(is.na(guild_genus), "None", guild_genus)) %>% - #mutate(guild_species = ifelse(is.na(guild_species), "None", guild_species)) - -#Guild status of all oomyc genera and subets of pp genera and other genera + unite(Plant_pathogenic, c(guild_genus, guild_species), + remove = FALSE) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "Plant Pathogen_Plant Pathogen", "++")) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "Plant Pathogen_NA", "+")) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "NA_NA", "-")) #%>% + +# Guild status of all oomyc genera and subets of pp genera and other genera genus_list <- ASVs_funguild %>% dplyr::group_by(Genus, guild_genus) %>% summarise() @@ -350,22 +411,23 @@ genera_pp <- filter(genus_list, guild_genus == "Plant Pathogen") %>% unlist() %>% unname() -genera_other <- filter(genus_list, guild_genus != "Plant Pathogen" | is.na(guild_genus)) %>% +genera_other <- filter(genus_list, + guild_genus != "Plant Pathogen" | is.na(guild_genus)) %>% select(Genus) %>% unlist() %>% unname() -#Subset by vector of taxonomy -##CHANGE me to a vector containing taxonomic terms to subset on +# Subset by vector of taxonomy +## CHANGE me to a vector containing taxonomic terms to subset on subVector <- c("Oomycetes") -##CHANGE ME to the taxonomic level at which you want to search for an entry +## CHANGE ME to the taxonomic level at which you want to search for an entry subTax <- "Class" subASVs <- cuphyr::list_subset_ASVs(subv = subVector, taxlvlsub = subTax) ps.subs <- prune_taxa(subASVs, ps) ps.transsub <- prune_taxa(subASVs, ps.trans) -#Subset by sample descriptor +# Subset by sample descriptor ps.bait <- subset_samples(ps, Bait == "Enriched") ps.untr <- subset_samples(ps, Bait == "Before enrichment") samps.bait <- sample_names(ps.bait) @@ -375,15 +437,20 @@ ps.untr <- prune_samples(samps.untr, ps) ps.bait.trans <- prune_samples(samps.bait, ps.trans) ps.untr.trans <- prune_samples(samps.untr, ps.trans) -#Create subset lists -subASVsAlg <- cuphyr::list_subset_ASVs(subv = c("Chrysophyceae"), taxlvlsub = "Class") -subASVsOom <- cuphyr::list_subset_ASVs(subv = c("Oomycetes"), taxlvlsub = "Class") -subASVsPP <- cuphyr::list_subset_ASVs(subv = c(genera_pp), taxlvlsub = "Genus") -subASVs_uncultured <- cuphyr::list_subset_ASVs(subv = c("uncultured"), taxlvlsub = "Genus") -#remove 'uncultured' entries from the PP ASVs, since these should not be counted as PP. +# Create subset lists +subASVsAlg <- cuphyr::list_subset_ASVs(subv = c("Chrysophyceae"), + taxlvlsub = "Class") +subASVsOom <- cuphyr::list_subset_ASVs(subv = c("Oomycetes"), + taxlvlsub = "Class") +subASVsPP <- cuphyr::list_subset_ASVs(subv = c(genera_pp), + taxlvlsub = "Genus") +subASVs_uncultured <- cuphyr::list_subset_ASVs(subv = c("uncultured"), + taxlvlsub = "Genus") +# remove 'uncultured' entries from the PP ASVs, since these should not be +# counted as PP. subASVsPP <- setdiff(subASVsPP, subASVs_uncultured) -#Taxonomic subset on sample descriptor subset +# Taxonomic subset on sample descriptor subset ps.b.tr.Alg <- prune_taxa(subASVsAlg, ps.bait.trans) ps.u.tr.Alg <- prune_taxa(subASVsAlg, ps.untr.trans) ps.b.Alg <- prune_taxa(subASVsAlg, ps.bait) @@ -395,7 +462,7 @@ ps.u.tr.PP <- prune_taxa(subASVsPP, ps.untr.trans) ps.b.PP <- prune_taxa(subASVsPP, ps.bait) ps.u.PP <- prune_taxa(subASVsPP, ps.untr) -#Rank some ps objects +# Rank some ps objects ranksBaitAlgae <- cuphyr::make_ranked_sums(ps.b.tr.Alg, myset = "Chrysophyceae") ranksUntrAlgae <- cuphyr::make_ranked_sums(ps.u.tr.Alg, myset = "Chrysophyceae") ranksBaitOom <- cuphyr::make_ranked_sums(ps.b.tr.Oom, myset = "All Oomycetes") @@ -403,28 +470,32 @@ ranksUntrOom <- cuphyr::make_ranked_sums(ps.u.tr.Oom, myset = "All Oomycetes") ranksBaitPP <- cuphyr::make_ranked_sums(ps.b.tr.PP, myset = "PP") ranksUntrPP <- cuphyr::make_ranked_sums(ps.u.tr.PP, myset = "PP") -ranksBaitAlgae_tot <- cuphyr::make_ranked_sums(ps.b.Alg, myset = "Chrysophyceae_tot") -ranksUntrAlgae_tot <- cuphyr::make_ranked_sums(ps.u.Alg, myset = "Chrysophyceae_tot") +ranksBaitAlgae_tot <- cuphyr::make_ranked_sums(ps.b.Alg, + myset = "Chrysophyceae_tot") +ranksUntrAlgae_tot <- cuphyr::make_ranked_sums(ps.u.Alg, + myset = "Chrysophyceae_tot") ranksBaitPP_tot <- cuphyr::make_ranked_sums(ps.b.PP, myset = "PP_tot") ranksUntrPP_tot <- cuphyr::make_ranked_sums(ps.u.PP, myset = "PP_tot") -###rerank tables for combined figures +### rerank tables for combined figures OomRankPP <- ranksBaitOom -#Adopt rank number from PP so that the sample order is the same +# Adopt rank number from PP so that the sample order is the same OomRankPP$Rank <- ranksBaitPP$Rank OomRankPP$Set <- "Other Oomycetes" -#Analogous to above +# Analogous to above UntrOomRankPP <- ranksUntrOom UntrOomRankPP$Rank <- ranksUntrPP$Rank UntrOomRankPP$Set <- "Other Oomycetes" AlgaeInvRankOom <- ranksBaitAlgae AlgaeInvRankOom$Rank <- ranksBaitPP$Rank -#Add ASV counts so that Chrysophyceae appear above the total Oomycete bars in the overlay +# Add ASV counts so that Chrysophyceae appear above the total Oomycete bars in +# the overlay AlgaeInvRankOom$Abundance <- AlgaeInvRankOom$Abundance + OomRankPP$Abundance UntrAlgaeInvRankOom <- ranksUntrAlgae UntrAlgaeInvRankOom$Rank <- ranksUntrPP$Rank -UntrAlgaeInvRankOom$Abundance <- UntrAlgaeInvRankOom$Abundance + UntrOomRankPP$Abundance -#Oom without PP +UntrAlgaeInvRankOom$Abundance <- UntrAlgaeInvRankOom$Abundance + + UntrOomRankPP$Abundance +# Oom without PP ranksBaitOwoP <- OomRankPP ranksBaitOwoP$Set <- "Other Oomycetes" ranksBaitOwoP$Abundance <- ranksBaitOwoP$Abundance - ranksBaitPP$Abundance @@ -435,15 +506,18 @@ ranksUntrOwoP$Set <- "Other Oomycetes" ranksUntrOwoP$Abundance <- ranksUntrOwoP$Abundance - ranksUntrPP$Abundance ranksUntrOwoP <- ranksUntrOwoP[order(-ranksUntrOwoP$Abundance),] ranksUntrOwoP$Rank <- c(1:nrow(ranksUntrOwoP)) -#Make dummy tables for 'other' ASV counts that fills up the sample bars to 1 and contains the total ASV count for ASVs other than Oomycetes and Chrysophyceae in the column 'OtherASVcounts' +# Make dummy tables for 'other' ASV counts that fills up the sample bars to 1 +# and contains the total ASV count for ASVs other than Oomycetes and +# Chrysophyceae in the column 'OtherASVcounts' OtherASVcounts <- 1 - AlgaeInvRankOom$Abundance OthersRankPP <- cbind(AlgaeInvRankOom, OtherASVcounts) OthersRankPP$Abundance <- 1 OthersRankPP$Set <- "Other classes" -#Rank Other ASVs for untreated and enriched samples to display in B and C respectively +# Rank Other ASVs for untreated and enriched samples to display in B and C +# respectively rankedOthersBait <- OthersRankPP[order(-OthersRankPP$OtherASVcounts),] rankedOthersBait$Rank <- c(1:nrow(rankedOthersBait)) -#Analogous to above +# Analogous to above OtherASVcounts <- 1 - UntrAlgaeInvRankOom$Abundance UntrOthersRankPP <- cbind(UntrAlgaeInvRankOom, OtherASVcounts) UntrOthersRankPP$Abundance <- 1 @@ -457,20 +531,23 @@ if (roottree == "TRUE") { new.tree <- ape::root(my.tree, outgroup = out.group, resolve.root = TRUE) phy_tree(ps) <- new.tree} -#export ps in clean table format for Supplementary Material +# export ps in clean table format for Supplementary Material ps_tbl_wide <- ps_tbl %>% arrange(Soil, Bait) %>% - pivot_wider(id_cols = c(OTU, Kingdom, Phylum, Class, Order, Family, Genus, Species), + pivot_wider(id_cols = c(OTU, Kingdom, Phylum, + Class, Order, Family, + Genus, Species), names_from = c(Soil, Bait), values_from = Abundance) ps_tbl_export <- ps_tbl_wide %>% mutate(num_ASV = as.numeric(str_remove(OTU, "ASV"))) %>% arrange(num_ASV) %>% select(-num_ASV) %>% dplyr::rename(ASV = OTU) -#export as csv +# export as csv write_csv(ps_tbl_export, path = file.path(path, "Supplementary_table_2.csv")) -#Optional: export as xlsx, requires 'writexl' package -#writexl::write_xlsx(ps_tbl_export, path = file.path(path, "Supplementary table 2.xlsx")) +# Optional: export as xlsx, requires 'writexl' package +#writexl::write_xlsx(ps_tbl_export, path = file.path(path, +# "Supplementary table 2.xlsx")) cat("Chunk successfully run") ``` @@ -485,25 +562,32 @@ The chunks below will produce various plots and other output. Each chunk is head This chunk sets the background structure and color palette. Viridis was chosen because it is optimized for grey-scale printing and various types of color blindness and More info on the Viridis palette can be found on [the Viridis info page](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html). ```{r plot design global, warning=FALSE, tidy=FALSE} -##CHANGE ME to preferred ggplot2 theme. Recommended: "theme_bw()". +## CHANGE ME to preferred ggplot2 theme. Recommended: "theme_bw()". theme_set(theme_bw()) my_scale_col <- scale_color_viridis(discrete = TRUE) my_scale_fill <- scale_fill_viridis(discrete = TRUE) -#Custom, more narrow color ranges based on viridis -#Base order to have adjacent colors be distinct from each other +# Custom, more narrow color ranges based on viridis +# Base order to have adjacent colors be distinct from each other sort_colors <- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20))) -#Customized vectors +# Customized vectors n_col <- 20 -viridis_greens <- viridis(n_col, option = "D", begin = 0.85 ,end = 0.7)[sort_colors] -viridis_reds <- viridis(n_col, option = "B", begin = 0.7 ,end = 0.5)[sort_colors] -viridis_blues <- viridis(n_col, option = "D", begin = 0.2 ,end = 0.4)[sort_colors] -viridis_yellows <- viridis(n_col, option = "D", begin = 1 ,end = 0.9)[sort_colors] -viridis_dark <- viridis(n_col, option = "A", begin = 0 ,end = 0.1)[sort_colors] -viridis_light <- viridis(n_col, option = "A", begin = 1 ,end = 0.9)[sort_colors] -sub_viridis <- list(viridis_greens, viridis_blues, viridis_yellows, viridis_light, viridis_reds, viridis_dark) +viridis_greens <- viridis(n_col, option = "D", + begin = 0.85 ,end = 0.7)[sort_colors] +viridis_reds <- viridis(n_col, option = "B", + begin = 0.7 ,end = 0.5)[sort_colors] +viridis_blues <- viridis(n_col, option = "D", + begin = 0.2 ,end = 0.4)[sort_colors] +viridis_yellows <- viridis(n_col, option = "D", + begin = 1 ,end = 0.9)[sort_colors] +viridis_dark <- viridis(n_col, option = "A", + begin = 0 ,end = 0.1)[sort_colors] +viridis_light <- viridis(n_col, option = "A", + begin = 1 ,end = 0.9)[sort_colors] +sub_viridis <- list(viridis_greens, viridis_blues, viridis_yellows, + viridis_light, viridis_reds, viridis_dark) cat("Chunk successfully run") ``` @@ -512,14 +596,17 @@ cat("Chunk successfully run") This chunk generates an overview over the positive controls (Supplementary Fig. 2) -```{r Positive controls} -##CHANGE ME to the secondary parameter of interest (categories on the x-axis). Accepted values are the column headers in your descriptor file. Uncomment to not use global value +```{r Positive controls, tidy=FALSE} +## CHANGE ME to the secondary parameter of interest (categories on the x-axis). +# Accepted values are the column headers in your descriptor file. comboPar <- "Soil" -#CHANGE ME to the primary parameter of interest (separate panels). Accepted values are the column headers in your descriptor file. Uncomment to not use global value +# CHANGE ME to the primary parameter of interest (separate panels). +# Accepted values are the column headers in your descriptor file. treePar <- "Run" -##CHANGE ME to the taxonomic level of interest (color coding). Accepted values are the column headers in your descriptor file. Uncomment to not use global value +## CHANGE ME to the taxonomic level of interest (color coding). +# Accepted values are the column headers in your descriptor file. taxlvltre <- "OTU" taxlvlbar <- "OTU" @@ -530,11 +617,31 @@ hp <- 22 #CHANGE ME to change the resolution (in dpi) of the output. res <- 300 -topnpplot <- plot_bar(ps.contr, x = comboPar, fill = taxlvlbar) + scale_fill_viridis_d() + theme(axis.title.x = element_blank(), legend.position = "none", legend.key.size = unit(3, "mm")) + ylab("ASV counts") + guides(col = guide_legend(ncol = 3)) + labs(fill = "ASV") -topntplot <- plot_bar(ps.transcont, x = comboPar, fill = taxlvlbar) + scale_fill_viridis_d() + theme(axis.title.x = element_blank(), legend.position = "none", legend.key.size = unit(3, "mm")) + ylab("Relative abundance") + guides(col = guide_legend(ncol = 3)) + labs(fill = "ASV") -tre <- plot_tree(ps.transcont, ladderize = "left", label.tips = taxlvltre, color = "abundance", text.size = 2.5, shape = "Run") + scale_color_viridis_c(aesthetics = c("color","fill")) + theme(legend.position = "left", panel.border = element_blank()) +topnpplot <- plot_bar(ps.contr, x = comboPar, fill = taxlvlbar) + + scale_fill_viridis_d() + + theme(axis.title.x = element_blank(), legend.position = "none", + legend.key.size = unit(3, "mm")) + ylab("ASV counts") + + guides(col = guide_legend(ncol = 3)) + labs(fill = "ASV") + +topntplot <- plot_bar(ps.transcont, x = comboPar, fill = taxlvlbar) + + scale_fill_viridis_d() + + theme(axis.title.x = element_blank(), + legend.position = "none", legend.key.size = unit(3, "mm")) + + ylab("Relative abundance") + + guides(col = guide_legend(ncol = 3)) + + labs(fill = "ASV") + +tre <- plot_tree(ps.transcont, ladderize = "left", + label.tips = taxlvltre, color = "abundance", + text.size = 2.5, shape = "Run") + + scale_color_viridis_c(aesthetics = c("color","fill")) + + theme(legend.position = "left", panel.border = element_blank()) -combocontr <- ggarrange(tre, ggarrange(topnpplot, topntplot, ncol = 2, labels = c("B", "C"), align = "hv", common.legend = TRUE, legend = "right"), nrow = 2, legend = "right", labels = c("A")) +combocontr <- ggarrange(tre, ggarrange(topnpplot, topntplot, + ncol = 2, labels = c("B", "C"), + align = "hv", common.legend = TRUE, + legend = "right"), nrow = 2, + legend = "right", labels = c("A")) save_plot(combocontr, plot_name = "Barplot_controls_combined_supfig2") @@ -547,14 +654,15 @@ cat("Chunk successfully run") This chunk generate a phylogenetic tree of the positive control sequences and ASVs detected in the positive controls. -```{r tree_seqs_contr} -##CHANGE ME to change the width (in cm) of the output. +```{r tree_seqs_contr, tidy=FALSE} +## CHANGE ME to change the width (in cm) of the output. wp <- 20 -#CHANGE ME to change the height (in cm) of the output. +# CHANGE ME to change the height (in cm) of the output. hp <- 15 real_contr <- refseq(ps.contr) -seqs <- readDNAStringSet(file = "Data_files/Pos_contrseqs.fasta", format = "fasta") +seqs <- readDNAStringSet(file = "Data_files/Pos_contrseqs.fasta", + format = "fasta") seqs <- DNAStringSet(c(real_contr, seqs)) align <- AlignSeqs(DNAStringSet(seqs), anchor = NA) seqs_phang <- phangorn::phyDat(as(align, "matrix"), type = "DNA") @@ -562,15 +670,20 @@ seqs_dm <- phangorn::dist.ml(seqs_phang) seqs_treeNJ <- NJ(seqs_dm) seqs_fit = pml(seqs_treeNJ, data = seqs_phang) fitGTRseqs <- update(seqs_fit, k = 4, inv = 0.2) -fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, optGamma = TRUE, - rearrangement = "stochastic", control = pml.control(trace = 0)) +fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, + optGamma = TRUE, + rearrangement = "stochastic", + control = pml.control(trace = 0)) out.group <- pick_new_outgroup(fitGTRseqs$tree) -new.tree <- ape::root(fitGTRseqs$tree, outgroup = out.group, resolve.root = TRUE) +new.tree <- ape::root(fitGTRseqs$tree, outgroup = out.group, + resolve.root = TRUE) fitGTRseqs_outgroup <- new.tree -#GTR_tree <- treeio::as.treedata(fitGTRseqs, type="ml") -kontr_ASV_tree <- ggtree(fitGTRseqs_outgroup) + geom_tree() + geom_treescale(x = 1) + geom_tiplab() + xlim(0,4) +# GTR_tree <- treeio::as.treedata(fitGTRseqs, type="ml") +kontr_ASV_tree <- ggtree(fitGTRseqs_outgroup) + + geom_tree() + geom_treescale(x = 1) + geom_tiplab() + xlim(0,4) + save_plot(kontr_ASV_tree, plot_name = "Pos-ASVs_kontr-seqs_tree") kontr_ASV_tree @@ -581,30 +694,33 @@ cat("Chunk successfully run") This chunk generates an overview over read counts for all samples and additional info on the data (Supplementary Fig. 1). ASV counts are summed up for all samples in the provided phyloseq object and assigned a rank based on this sum. The sample with the highest amount of total ASV counts will receive rank 1, the one with the second highest rank 2 and so on. A bar plot is generated, plotting the total ASV counts against the sample rank with color filling by run and a dashed line indicating the chosen cutoff defined in minASVcount (Parameter chunk). -```{r Averages} -##CHANGE ME to change the width (in cm) of the output. +```{r Averages, tidy=FALSE} +## CHANGE ME to change the width (in cm) of the output. wp = 20 -#CHANGE ME to change the height (in cm) of the output. +# CHANGE ME to change the height (in cm) of the output. hp = 15 -#CHANGE ME to change the resolution (in dpi) of the output. +# CHANGE ME to change the resolution (in dpi) of the output. res = 300 -#Rank all samples, including those that were discarded from 'ps', but excluding controls +# Rank all samples, including those that were discarded from 'ps', +# but excluding controls all <- cuphyr::make_ranked_sums(ps.raw, myset = "Total") -#This calculates an average read count for all samples that can be added to the plot as e.g. a dashed line or text +# This calculates an average read count for all samples that can be added to +# the plot as e.g. a dashed line or text avg <- mean(all$Abundance) pall <- ggplot(data = all, aes(y = Abundance, x = Samples)) -#plot figure +# plot figure overview <- ggplot(data = all, aes(x = Rank, y = Abundance, fill = Run)) + geom_col() + my_scale_fill + geom_hline(yintercept = minASVcount, linetype = "dashed") + ylab("Total ASV counts ('reads')") -#Also give the number of ASVs, oomycete ASVs and calculate the percentage of ASVs identified as oomycetes +# Also give the number of ASVs, oomycete ASVs and calculate the percentage of +# ASVs identified as oomycetes oomlist <- cuphyr::list_subset_ASVs(subv = c("Oomycetes"), taxlvlsub = "Class") -#Save figure +# Save figure save_plot(overview, plot_name = "Overview_SupFig1") overview @@ -620,33 +736,42 @@ cat("Chunk successfully run") This chunk plots abundance of the Top n ASVs and taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. In the manuscript, the top 5 classes and top 10 genera are shown in Fig. 1. ```{r Bar plot, tidy=FALSE} -#CHANGE ME to the amount of top taxlvl to plot (e.g. 'numt = 20' plots Top20 Taxa at taxlvl) +# CHANGE ME to the amount of top taxlvl to plot (e.g. 'numt = 20' plots Top20 +# Taxa at taxlvl) numt <- 5 -#CHANGE ME to the amount of top taxa you want to plot (e.g. 'numt2 = 25' plots Top25 Taxa at taxlvl2) +# CHANGE ME to the amount of top taxa you want to plot (e.g. 'numt2 = 25' plots +# Top25 Taxa at taxlvl2) numt2 <- 10 -#CHANGE ME to the primary parameter of interest (x-axis). Accepted values are the column headers in the descriptor file. +# CHANGE ME to the primary parameter of interest (x-axis). Accepted values are +# the column headers in the descriptor file. primaryPar <- "Soil" -##CHANGE ME to the secondary parameter of interest (panels). Accepted values are the column headers in the descriptor file. +## CHANGE ME to the secondary parameter of interest (panels). Accepted values +# are the column headers in the descriptor file. secondaryPar <- "Bait" -##CHANGE ME to the taxonomic level of interest (color coding). Accepted values are available taxonomic levels. 'taxlvl' should be higher than 'taxlvl2', e.g. Class and Genus +## CHANGE ME to the taxonomic level of interest (color coding). Accepted values +# are available taxonomic levels. 'taxlvl' should be higher than 'taxlvl2', e.g. +# Class and Genus taxlvl <- "Class" taxlvl2 <- "Genus" -#Adds formatted legend labels for the specific output in the manuscript. WARNING! Will overwrite actual values and needs to be turned off/adjusted manually when data is updated +# Adds formatted legend labels for the specific output in the manuscript. +# WARNING! Will overwrite actual values and needs to be turned off/adjusted +# manually when data is updated customLabels <- TRUE -##CHANGE ME to change the width (in cm) of the output. +## CHANGE ME to change the width (in cm) of the output. wp <- 20 -#CHANGE ME to change the height (in cm) of the output. +# CHANGE ME to change the height (in cm) of the output. hp <- 15 -#CHANGE ME to change the resolution (in dpi) of the output. +# CHANGE ME to change the resolution (in dpi) of the output. res <- 300 -#Get total number of reads and number of Pythium/Phytophthora reads (without contro samples!) to find out percentage of Pyhium and Phytophthora +# Get total number of reads and number of Pythium/Phytophthora reads (without +# control samples!) to find out percentage of Pyhium and Phytophthora total_reads_no_contr <- ps_tbl %>% dplyr::filter(Pass == "Yes") %>% dplyr::mutate(sumAbu = sum(Abundance)) %>% @@ -684,7 +809,7 @@ prctg_pyth_phyt_str <- paste0("\n\nTotal number of reads in the samples: ", "%\nPercentage of Phytophthora total: ", phytoph_reads_no_contr/total_reads_no_contr*100, "%") -#Most abundant at specified levels, phyloseq objects +# Most abundant at specified levels, phyloseq objects ps.topnt <- cuphyr::abundant_tax_physeq( physeq = ps.trans, lvl = taxlvl, top = numt, ignore_na = TRUE, silent = FALSE) @@ -692,7 +817,7 @@ ps.topnt2 <- cuphyr::abundant_tax_physeq( physeq = ps.trans,lvl = taxlvl2, top = numt2, ignore_na = TRUE, silent = FALSE) -#Abundance lists (sorted by abundance) +# Abundance lists (sorted by abundance) taxlist_topnt1 <- cuphyr::abundant_tax_physeq( physeq = ps.trans, lvl = taxlvl, top = numt, output_format = "tops", ignore_na = TRUE) @@ -700,7 +825,8 @@ taxlist_topnt2 <- cuphyr::abundant_tax_physeq( physeq = ps.trans, lvl = taxlvl2, top = numt2, output_format = "tops", ignore_na = TRUE) -#tidy evaluation translation magic (needed so the dplyr functions that extract 'tax_lookup' below can parse the strings from the variable) +# tidy evaluation translation magic (needed so the dplyr functions that extract +# 'tax_lookup' below can parse the strings from the variable) lvl1 <- sym(taxlvl) lvl2 <- sym(taxlvl2) lvl1 <- enquo(lvl1) @@ -712,11 +838,15 @@ tax_lookup <- ps_tbl %>% summarise() %>% filter(!!lvl2 %in% taxlist_topnt2) -#Define fixed color schemes for classes for consistency between plots. This is a color scheme specified for this particular published figure. Can be used for orientation in general cases. -#Background colors to be overwritten for special cases -colors_lvl1 <- viridis(length(taxlist_topnt1), option = "D", begin = 1 , end = 0.5) +# Define fixed color schemes for classes for consistency between plots. This is +# a color scheme specified for this particular published figure. Can be used for +# orientation in general cases. +# Background colors to be overwritten for special cases +colors_lvl1 <- viridis(length(taxlist_topnt1), + option = "D", begin = 1 , end = 0.5) names(colors_lvl1) <- taxlist_topnt1 -colors_lvl2 <- viridis(length(taxlist_topnt2), option = "D", begin = 1 , end = 0.5) +colors_lvl2 <- viridis(length(taxlist_topnt2), + option = "D", begin = 1 , end = 0.5) names(colors_lvl2) <- taxlist_topnt2 colors_combo <- c(colors_lvl1, colors_lvl2) @@ -787,7 +917,8 @@ topnt2_summary <- cuphyr::summarise_physeq(physeq = ps, paste0("top ", numt2, " ", taxlvl2), samp_names = FALSE) #Combine the plots -combobar1 <- ggarrange(topnt, topnt2, nrow = 2, labels = c("A", "B"), align = "hv") +combobar1 <- ggarrange(topnt, topnt2, nrow = 2, + labels = c("A", "B"), align = "hv") #Save save_plot(combobar1, plot_name = "Topn_barplot_Fig1") @@ -802,29 +933,45 @@ cat("Chunk successfully run") #### Functional classification using the FUNGuild database This chunk imports the FUNGuild database based on code by [S. Faye Smith (2018)](https://rpubs.com/faysmith/metabarcoding), then classifies and plots all oomycete genera in the data set (Fig. 2). -```{r funguild_classification} -#Plot save dimensions +```{r funguild_classification, tidy=FALSE} +# Plot save dimensions wp <- 20 hp <- 15 res <- 300 -#Adds formatted legend labels for the specific output in the manuscript. WARNING! Will overwrite actual values and needs to be turned off/adjusted manually when data is updated +# Adds formatted legend labels for the specific output in the manuscript. +# WARNING! Will overwrite actual values and needs to be turned off/adjusted +# manually when data is updated customLabels <- TRUE -#Custom color coding to emphasize pp genera, particularly Pythium and Phytophthora -colors_genera_pp <- viridis(length(genera_pp), option = "D", begin = 0 , end = 0.8) +# Custom color coding to emphasize pp genera, particularly Pythium and +# Phytophthora +colors_genera_pp <- viridis(length(genera_pp), option = "D", + begin = 0 , end = 0.8) names(colors_genera_pp) <- genera_pp -colors_genera_other <- viridis(length(genera_other), option = "D", begin = 1 ,end = 0.9) +colors_genera_other <- viridis(length(genera_other), option = "D", + begin = 1 , end = 0.9) names(colors_genera_other) <- genera_other colors_oomyc_genera <- c(colors_genera_pp, colors_genera_other) -#Sort alphabetically by vector names to avoid mixing up color assignments (origin of issue not resolved) + +# Sort alphabetically by vector names to avoid mixing up color assignments +# (origin of issue not resolved) colors_oomyc_genera <- colors_oomyc_genera[order(names(colors_oomyc_genera))] colors_oomyc_genera["Phytophthora"] <- viridis_reds[1] colors_oomyc_genera["Pythium"] <- viridis_reds[4] #define ggplot theme (shared between genus and species level plots) -shared_theme <- theme(legend.key.size = unit(3, "mm"), legend.position = "none", axis.title.x = element_blank(), plot.margin = margin(20,10,10,10), legend.text.align = 0) -shared_theme_abu <- theme(legend.key.size = unit(3, "mm"), legend.position = "none", axis.title.x = element_blank(), plot.margin = margin(20,10,10,10),legend.text.align = 0) +shared_theme <- theme(legend.key.size = unit(3, "mm"), + legend.position = "none", + axis.title.x = element_blank(), + plot.margin = margin(20,10,10,10), + legend.text.align = 0) + +shared_theme_abu <- theme(legend.key.size = unit(3, "mm"), + legend.position = "none", + axis.title.x = element_blank(), + plot.margin = margin(20,10,10,10), + legend.text.align = 0) #Format legend labels if(customLabels){ @@ -848,33 +995,42 @@ if(customLabels){ labelling <- unique(ASVs_funguild$Genus) } -guild_g <- ggplot(ASVs_funguild, aes(x = guild_genus, fill = Genus)) + geom_bar() + shared_theme + - scale_fill_manual(values = colors_oomyc_genera, na.value = "grey", labels = labelling) + +guild_g <- ggplot(ASVs_funguild, aes(x = guild_genus, fill = Genus)) + + geom_bar() + shared_theme + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + ylab("Number of ASVs") + ylim(0,1500) -guild_s <- ggplot(ASVs_funguild, aes(x = guild_species, fill = Genus)) + geom_bar() + shared_theme + - scale_fill_manual(values = colors_oomyc_genera, na.value = "grey", labels = labelling) + + +guild_s <- ggplot(ASVs_funguild, aes(x = guild_species, fill = Genus)) + + geom_bar() + shared_theme + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + ylab("Number of ASVs") + ylim(0,1500) -guild_g_abu <- ggplot(ASVs_funguild, aes(x = guild_genus, y = Abundance_sum/1000000, fill = Genus)) + +guild_g_abu <- ggplot(ASVs_funguild, aes(x = guild_genus, + y = Abundance_sum/1000000, + fill = Genus)) + geom_col() + shared_theme_abu + - scale_fill_manual(values = colors_oomyc_genera, na.value = "grey", labels = labelling) + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + ylab("Abundance in M reads") + ylim(0,2.5) -guild_s_abu <- ggplot(ASVs_funguild, aes(x = guild_species, y = Abundance_sum/1000000, fill = Genus)) + +guild_s_abu <- ggplot(ASVs_funguild, aes(x = guild_species, + y = Abundance_sum/1000000, + fill = Genus)) + geom_col() + shared_theme_abu + - scale_fill_manual(values = colors_oomyc_genera, na.value = "grey", labels = labelling) + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + ylab("Abundance in M reads") + ylim(0,2.5) - - -guilds <- ggarrange(guild_g, guild_g_abu, guild_s, guild_s_abu, nrow = 2, ncol = 2, +guilds <- ggarrange(guild_g, guild_g_abu, guild_s, + guild_s_abu, nrow = 2, ncol = 2, common.legend = TRUE, legend = "right" ,align = "hv", - labels = c("Genus level", "", "Species level", ""), font.label = list(size = 14)) + labels = c("Genus level", "", "Species level", ""), + font.label = list(size = 14)) guilds save_plot(guilds ,plot_name = "FunGuild_combo_Fig2") - - cat("Chunk successfully run") ``` @@ -882,27 +1038,38 @@ cat("Chunk successfully run") This chunk generates all panels of Fig. 3, in which the relative abundances of taxonomic classes of interest are summarized. Relative abundances are summed up for all samples in the provided phyloseq objects and assigned a rank based on this sum. The sample with the highest amount of total ASV counts will receive rank 1, the one with the second highest rank 2 and so on. A bar plot is generated, plotting relative abundance against the sample rank with the bars being colored according to the taxonomic. This is supposed to provide a quick overview of relative abundance for the taxonomic class subsets. ```{r ranked ASV counts for groups of interest, tidy=FALSE, warning=FALSE} -##CHANGE ME to change the width (in cm) of the output. +## CHANGE ME to change the width (in cm) of the output. wp <- 17.5 -#CHANGE ME to change the height (in cm) of the output. +# CHANGE ME to change the height (in cm) of the output. hp <- 15 -#CHANGE ME to change the resolution (in dpi) of the output. +# CHANGE ME to change the resolution (in dpi) of the output. res <- 300 -#calculate averages for display in graphs -avgNames <- c("PPUntr", "PPBait","ChrUntr", "ChrBait", "O-PUntr", "O-PBait", "OthUntr", "OthBait") -avgs <- c(mean(ranksUntrPP$Abundance),mean(ranksBaitPP$Abundance), - mean(ranksUntrAlgae$Abundance),mean(ranksBaitAlgae$Abundance), - mean(ranksUntrOwoP$Abundance),mean(ranksBaitOwoP$Abundance), - mean(rankedOthersUntr$OtherASVcounts),mean(rankedOthersBait$OtherASVcounts)) +# calculate averages for display in graphs +avgNames <- c("PPUntr", "PPBait","ChrUntr", "ChrBait", + "O-PUntr", "O-PBait", "OthUntr", "OthBait") +avgs <- c(mean(ranksUntrPP$Abundance), + mean(ranksBaitPP$Abundance), + mean(ranksUntrAlgae$Abundance), + mean(ranksBaitAlgae$Abundance), + mean(ranksUntrOwoP$Abundance), + mean(ranksBaitOwoP$Abundance), + mean(rankedOthersUntr$OtherASVcounts), + mean(rankedOthersBait$OtherASVcounts)) + names(avgs) <- avgNames -#multiply to transform relative abundances into avg percentages and round to one decimal + +# multiply to transform relative abundances into avg percentages and round to +# one decimal avgs <- avgs*100 avgs <- format(round(avgs, 1), nsmall = 1) -#Define fixed color schemes for consistency between plots -plotvars <- c("Chrysophyceae", "All Oomycetes", "Other Oomycetes", "Other classes", "PP") -#Just to catch others/exceptions, make a general palette that will be overwritten below +# Define fixed color schemes for consistency between plots +plotvars <- c("Chrysophyceae", "All Oomycetes", + "Other Oomycetes", "Other classes", "PP") + +# Just to catch others/exceptions, make a general palette that will be +# overwritten below plotPalette <- viridis(length(plotvars)) names(plotPalette) <- plotvars plotPalette["Other classes"] <- "#C0C0C0" @@ -911,43 +1078,122 @@ plotPalette["PP"] <- viridis_reds[4] plotPalette["Other Oomycetes"] <- viridis_reds[1] plotPalette["All Oomycetes"] <- viridis_reds[1] -#plot total ASV counts, sorted by rank and color by class. -AllBait <- ggplot(data = OthersRankPP, aes(x = Rank, y = Abundance, fill = Set)) -AllUntr <- ggplot(data = UntrOthersRankPP, aes(x = Rank, y = Abundance, fill = Set)) +# plot total ASV counts, sorted by rank and color by class. +AllBait <- ggplot(data = OthersRankPP, + aes(x = Rank, y = Abundance, fill = Set)) +AllUntr <- ggplot(data = UntrOthersRankPP, + aes(x = Rank, y = Abundance, fill = Set)) + +customTheme <- theme(legend.position = "none", + axis.title = element_blank(), + title = element_text(size = 6)) +customThemeA <- theme(legend.position = "none", + legend.title = element_blank(), + legend.key.size = unit(5,"mm"), + axis.title.x = element_blank()) -customTheme <- theme(legend.position = "none", axis.title = element_blank(), title = element_text(size = 6)) -customThemeA <- theme(legend.position = "none", legend.title = element_blank(), legend.key.size = unit(5,"mm"), axis.title.x = element_blank()) xUpperLim <- nrow(OthersRankPP) + 1 ytextl = 0.05 xtextl = 15 ytexts = 0.75 xtexts = 45 -middleline <- geom_blank() #geom_hline(yintercept = 0.5, linetype="dashed", color="black") - -pAllBait <- AllBait + geom_col() + geom_col(data = AlgaeInvRankOom) + geom_col(data = OomRankPP) + geom_col(data = ranksBaitPP) + scale_fill_manual(values = plotPalette) + ggtitle("After enrichment") + xlab("Sample rank") + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + customThemeA + middleline + annotate(geom = 'text', label = paste("Mean PP", avgs["PPBait"], "%"), x = xtextl, y = ytextl) + ylab("rel. Abundance") - -pAllUntr <- AllUntr + geom_col() + geom_col(data = UntrAlgaeInvRankOom) + geom_col(data = UntrOomRankPP) + geom_col(data = ranksUntrPP) + scale_fill_manual(values = plotPalette) + ggtitle("Before enrichment") + xlab("Sample rank") + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + customThemeA + middleline + annotate(geom = 'text', label = paste("Mean PP", avgs["PPUntr"], "%"), x = xtextl, y = ytextl) + ylab("rel. Abundance") - -AlgBait <- ggplot(data = ranksBaitAlgae, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Chrysophyceae - after enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -OomBait <- ggplot(data = ranksBaitOwoP, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Other oomycetes - after enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["O-PBait"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -OthBait <- ggplot(data = rankedOthersBait, aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Other classes - after enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["OthBait"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -AlgUntr <- ggplot(data = ranksUntrAlgae, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Chrysophyceae - before enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -OomUntr <- ggplot(data = ranksUntrOwoP, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Other oomycetes - before enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["O-PUntr"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -OthUntr <- ggplot(data = rankedOthersUntr, aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Other classes - before enrichment") + xlim(0,xUpperLim) + customTheme + middleline + annotate(geom = 'text', label = paste("Mean", avgs["OthUntr"], "%"), x = xtexts, y = ytexts) + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) - -#A.combo <- ggarrange(pAllUntr, pAllBait, nrow=2, labels = c("A"), vjust = 1, common.legend = TRUE, legend = "bottom") +middleline <- geom_blank() #geom_hline(yintercept = 0.5, + # linetype="dashed", color="black") + +pAllBait <- AllBait + geom_col() + + geom_col(data = AlgaeInvRankOom) + + geom_col(data = OomRankPP) + + geom_col(data = ranksBaitPP) + + scale_fill_manual(values = plotPalette) + + ggtitle("After enrichment") + xlab("Sample rank") + + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + + customThemeA + middleline + + annotate(geom = 'text', label = paste("Mean PP", avgs["PPBait"], "%"), + x = xtextl, y = ytextl) + + ylab("rel. Abundance") + +pAllUntr <- AllUntr + geom_col() + + geom_col(data = UntrAlgaeInvRankOom) + + geom_col(data = UntrOomRankPP) + + geom_col(data = ranksUntrPP) + + scale_fill_manual(values = plotPalette) + + ggtitle("Before enrichment") + xlab("Sample rank") + + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + + customThemeA + middleline + + annotate(geom = 'text', label = paste("Mean PP", avgs["PPUntr"], "%"), + x = xtextl, y = ytextl) + + ylab("rel. Abundance") + +AlgBait <- ggplot(data = ranksBaitAlgae, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OomBait <- ggplot(data = ranksBaitOwoP, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other oomycetes - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["O-PBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OthBait <- ggplot(data = rankedOthersBait, + aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other classes - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["OthBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +AlgUntr <- ggplot(data = ranksUntrAlgae, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OomUntr <- ggplot(data = ranksUntrOwoP, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other oomycetes - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["O-PUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OthUntr <- ggplot(data = rankedOthersUntr, + aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other classes - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["OthUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +#A.combo <- ggarrange(pAllUntr, pAllBait, nrow=2, +# labels = c("A"), vjust = 1, common.legend = TRUE, legend = "bottom") untr_smallplots <- ggarrange(AlgUntr, OomUntr, OthUntr, nrow = 3) -untr_smallplots_anot <- annotate_figure(untr_smallplots, left = text_grob("rel. Abundance", rot = 90, size = 11)) +untr_smallplots_anot <- annotate_figure(untr_smallplots, + left = text_grob("rel. Abundance", + rot = 90, size = 11)) bait_smallplots <- ggarrange(AlgBait, OomBait, OthBait, nrow = 3) -bait_smallplots_anot <- annotate_figure(bait_smallplots, left = text_grob("rel. Abundance", rot = 90, size = 11)) +bait_smallplots_anot <- annotate_figure(bait_smallplots, + left = text_grob("rel. Abundance", + rot = 90, size = 11)) -combo <- ggarrange(pAllUntr, untr_smallplots_anot, pAllBait, bait_smallplots_anot, nrow = 2, ncol = 2, widths = c(2,1), labels = c("A", "", "B", ""), common.legend = TRUE, legend = "bottom") + theme(plot.margin = margin(10,10,10,10)) +combo <- ggarrange(pAllUntr, untr_smallplots_anot, pAllBait, + bait_smallplots_anot, nrow = 2, ncol = 2, widths = c(2,1), + labels = c("A", "", "B", ""), common.legend = TRUE, + legend = "bottom") + theme(plot.margin = margin(10,10,10,10)) #Save plot save_plot(combo, plot_name = "Class_ranking_Fig3") @@ -961,7 +1207,7 @@ cat("Chunk successfully run") This chunk documents the pairwise t-tests performed for the classes shown in Fig. 3. For each class/group of classes, the mean relative abundance before and after baiting are compared. -```{r t-test of relative abundance means per class} +```{r t-test of relative abundance means per class, tidy=FALSE} library(rstatix) pairwise_test <- function(ASV_list){ ps.temp <- prune_taxa(ASV_list, ps.trans) @@ -993,52 +1239,66 @@ cat("Chunk successfully run") This chunk generates a phylogeny for the isolates ITS sequences and corresponding ASVs and one reference sequence per identified species. The selection/similarity of ASVs was determined by vsearch, the fasta files were assembled with bash commands (Supplementary Fig. 4). ``` -#Trimming isolate sequences to the metabarcoding regions from Sanger consensus multifasta -cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 -o trim_isos_v3.fasta Okay_Consensus_seqs_v3.fa - -#Finding homologous ASVs using vsearch on a one-line-per-sequence (as opposed to multiline fasta) version of the ASV fasta with a similarity cutoff of 98% -vsearch --usearch_global trim_isos_v3.fasta -db ASVs_without_controls_oneline.fasta --userout match_trim-isolates_ASVs_v3.txt -userfields query+target+id+mism+opens -id 0.98 -strand both - -#Parsing results into a list of unique ASVs found +# Trimming isolate sequences to the metabarcoding regions from Sanger +# consensus multifasta +cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 \ + -o trim_isos_v3.fasta Okay_Consensus_seqs_v3.fa + +# Finding homologous ASVs using vsearch on a one-line-per-sequence +# (as opposed to multiline fasta) version of the ASV fasta with a similarity +# cutoff of 98% +vsearch --usearch_global trim_isos_v3.fasta -db ASVs_without_controls_oneline.fasta \ +--userout match_trim-isolates_ASVs_v3.txt -userfields query+target+id+mism+opens \ +-id 0.98 -strand both + +# Parsing results into a list of unique ASVs found sed 's/.*ASV/ASV/' match_trim-isolates_ASVs_v3.txt > exact_ASVs_v3.tmp sed -i 's/\t.*//' exact_ASVs_v3.tmp sort exact_ASVs_v3.tmp | uniq > exact_ASVs_v3.txt -#Removing temporary file +# Removing temporary file yes | rm exact_ASVs_v3.tmp -#Extracting fasta belonging to the unique ASVs from the oneline fasta file -grep -f exact_ASVs_v3.txt -A1 ASVs_without_controls_oneline.fasta > exact_ASVs_v3.fasta +# Extracting fasta belonging to the unique ASVs from the oneline fasta file +grep -f exact_ASVs_v3.txt -A1 ASVs_without_controls_oneline.fasta \ +> exact_ASVs_v3.fasta sed -i '/--/d' exact_ASVs_v3.fasta -#Concatenating ASVs and trimmed isolate sequences into a multi-sequence fasta +# Concatenating ASVs and trimmed isolate sequences into a multi-sequence fasta cat exact_ASVs_v3.fasta trim_isos_v3.fasta > iso_and_ASVs_v3.fasta -#Modifying fasta headers for better display in the phylogenetic tree +# Modifying fasta headers for better display in the phylogenetic tree sed -i 's/||.*//' iso_and_ASVs_v3.fasta sed -i 's/@.*//' iso_and_ASVs_v3.fasta -#Trim reference BLAST results from with Genbank identifiers to the region of interest and concatenate with the isolate and ASV sequences -cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 -o BLAST_ref_trim.fa BLAST_ref.fasta +# Trim reference BLAST results from with Genbank identifiers to the region of +# interest and concatenate with the isolate and ASV sequences +cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 \ +-o BLAST_ref_trim.fa BLAST_ref.fasta cat iso_and_ASVs_v3.fasta BLAST_ref_trim.fa > iso_ASVs_refs_v3.fasta ``` -```{r tree_seqs_Iso} +```{r tree_seqs_Iso, tidy=FALSE} wp <- 20 hp <- 15 -seqs <- readDNAStringSet(file = file.path(path,"iso_ASVs_refs_v3.fasta"), format = "fasta") +seqs <- readDNAStringSet(file = file.path(path,"iso_ASVs_refs_v3.fasta"), + format = "fasta") align <- AlignSeqs(DNAStringSet(seqs), iterations = 100, refinements = 5) seqs_phang <- phangorn::phyDat(as(align, "matrix"), type = "DNA") seqs_dm <- phangorn::dist.ml(seqs_phang) seqs_treeNJ <- NJ(seqs_dm) seqs_fit = pml(seqs_treeNJ, data = seqs_phang) fitGTRseqs <- update(seqs_fit, k = 4, inv = 0.2) -fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, optGamma = TRUE, - rearrangement = "stochastic", control = pml.control(trace = 0)) +fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, + optGamma = TRUE, rearrangement = "stochastic", + control = pml.control(trace = 0)) GTR_tree <- treeio::as.treedata(fitGTRseqs$tree, type = "ml") -iso_ASV_tree <- ggtree(GTR_tree) + geom_tree() + geom_treescale(x = 0.5) + geom_tiplab() + xlim(-0.1,3) +iso_ASV_tree <- ggtree(GTR_tree) + geom_tree() + + geom_treescale(x = 0.5) + + geom_tiplab() + xlim(-0.1,3) + save_plot(iso_ASV_tree, plot_name = "IsolateSeqs_ASVs_ref_tree_Supfig4") iso_ASV_tree cat("Chunk successfully run") @@ -1048,16 +1308,20 @@ cat("Chunk successfully run") This chunk generates a tile plot of the presence and abundance of ASVs that correspond with isolates in the soil samples before and after enrichment (Fig. 4). -```{r isolates-v-ASVs} +```{r isolates-v-ASVs, tidy=FALSE} ##CHANGE ME to change the width (in cm) of the output. wp <- 12 #CHANGE ME to change the height (in cm) of the output. hp <- 10 -#ASVs (before LULU!) that exactly match isolates seqs (identified by vsearch), read in from fasta +# ASVs (before LULU!) that exactly match isolates seqs (identified by vsearch), +# read in from fasta exASVs_seqs <- readDNAStringSet(paste0(path,"/exact_ASVs_v3.fasta")) iso_seqs <- readDNAStringSet(paste0(path,"/trim_isos_v3.fasta")) -iso_ASV_match <- read_delim(paste0(path,"/match_trim-isolates_ASVs_v3.txt"), delim = "\t" ,col_names = c("Iso_Soil" ,"OTU", "Match", "Mism", "Gap")) +iso_ASV_match <- read_delim(paste0(path,"/match_trim-isolates_ASVs_v3.txt"), + delim = "\t", + col_names = c("Iso_Soil" ,"OTU", + "Match", "Mism", "Gap")) #Parse headers to get ASV IDs listed in vector exASVs <- names(exASVs_seqs) %>% str_remove("\\|\\|.*") @@ -1080,7 +1344,9 @@ exASV_counts <- ps_tr_tbl %>% dplyr::filter(OTU %in% exASVs) %>% dplyr::filter(Abundance >= 1) -iso_ASVs <- ggplot(exASV_counts, aes(x = OTU, y = reorder(Soil, dplyr::desc(Soil)), fill = Prcnt_Abundance)) + +iso_ASVs <- ggplot(exASV_counts, aes(x = OTU, + y = reorder(Soil, dplyr::desc(Soil)), + fill = Prcnt_Abundance)) + geom_tile() + scale_fill_viridis() + facet_wrap(~Bait, labeller = labeller(Bait = enrich_labs)) + @@ -1119,35 +1385,78 @@ names(avgs) <- avgNames avgs <- format(round(avgs, 1), nsmall = 1) #Define fixed color schemes for consistency between plots -plotvars <- c("Chrysophyceae_tot", "All Oomycetes", "Other Oomycetes", "Other classes", "PP_tot") +plotvars <- c("Chrysophyceae_tot", "All Oomycetes", + "Other Oomycetes", "Other classes", "PP_tot") getPalette <- colorRampPalette(viridis(length(plotvars))) plotPalette <- getPalette(length(plotvars)) names(plotPalette) <- plotvars plotPalette["Chrysophyceae_tot"] <- viridis_yellows[2] plotPalette["PP_tot"] <- viridis_reds[4] -customTheme <- theme(legend.position = "none", axis.title = element_blank(), title = element_text(size = 7)) -customThemeA <- theme(legend.position = "none", legend.title = element_blank(), legend.key.size = unit(5,"mm"), axis.title = element_blank(), title = element_text(size = 10)) +customTheme <- theme(legend.position = "none", + axis.title = element_blank(), + title = element_text(size = 7)) +customThemeA <- theme(legend.position = "none", + legend.title = element_blank(), + legend.key.size = unit(5,"mm"), + axis.title = element_blank(), + title = element_text(size = 10)) + xUpperLim <- nrow(ranksBaitAlgae_tot) + 1 ytextl = 0.05 xtextl = 15 ytexts = 90000 xtexts = 45 -pAllBait <- ggplot(data = ranksBaitPP_tot, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("PP - after enrichment") + xlim(0,xUpperLim) + customTheme + annotate(geom = 'text', label = paste("Mean", avgs["PPBait"]), x = xtexts, y = ytexts) + scale_y_continuous(limits = c(0,105000)) - -pAllUntr <- ggplot(data = ranksUntrPP_tot, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("PP - before enrichment") + xlim(0,xUpperLim) + customTheme + annotate(geom = 'text', label = paste("Mean", avgs["PPUntr"]), x = xtexts, y = ytexts) + scale_y_continuous(limits = c(0,105000)) - -AlgBait <- ggplot(data = ranksBaitAlgae_tot, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Chrysophyceae - after enrichment") + xlim(0,xUpperLim) + customTheme + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"]), x = xtexts, y = ytexts) + scale_y_continuous(limits = c(0,105000)) - -AlgUntr <- ggplot(data = ranksUntrAlgae_tot, aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + scale_fill_manual(values = plotPalette) + ggtitle("Chrysophyceae - before enrichment") + xlim(0,xUpperLim) + customTheme + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"]), x = xtexts, y = ytexts) + scale_y_continuous(limits = c(0,105000)) +pAllBait <- ggplot(data = ranksBaitPP_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("PP - after enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["PPBait"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +pAllUntr <- ggplot(data = ranksUntrPP_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("PP - before enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["PPUntr"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +AlgBait <- ggplot(data = ranksBaitAlgae_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - after enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +AlgUntr <- ggplot(data = ranksUntrAlgae_tot, + aes(x = Rank, y = Abundance, fill = Set)) + + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - before enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) A.combo <- ggarrange(pAllUntr, pAllBait, nrow = 2, labels = c("A"), vjust = 1) B.combo <- ggarrange(AlgUntr, AlgBait, nrow = 2, labels = c("B"), vjust = 1) A.combo.anot <- annotate_figure(A.combo, left = text_grob("Abundance", rot = 90)) -combo.AB <- ggarrange(A.combo.anot, B.combo, ncol = 2) + theme(plot.margin = margin(10,10,10,10)) + scale_fill_manual(values = plotPalette) +combo.AB <- ggarrange(A.combo.anot, B.combo, ncol = 2) + + theme(plot.margin = margin(10,10,10,10)) + + scale_fill_manual(values = plotPalette) #Save plot diff --git a/SupMat3_submission_JoAE.html b/SupMat3_submission_JoAE.html index fa21f6fd30caef034bb88efb246ca1d3f66a4b31..8ff74cbf431c5c518cf76ef8262b27543c938976 100644 --- a/SupMat3_submission_JoAE.html +++ b/SupMat3_submission_JoAE.html @@ -1644,7 +1644,7 @@ summary { <div id="introduction" class="section level2"> <h2>Introduction</h2> <p>If all needed packages (see below) are installed and the entire Git repository belonging to the publication was cloned so that the file organization is identical, it should be possible to run and reproduce the entire data analysis using this R Markdown file without any changes or adjustments.</p> -<p>This is an R Markdown file containing code to document the data analysis for the manuscript “DNA metabarcoding reveals broad presence of potentially plant pathogenic oomycetes in cross-border transported soil”. Results of the DADA2 analysis are parsed into phyloseq and other, generic data objects and metadata is added. In addition to documenting the analysis, the analysis should be fully reproducible using the provided files at ##GITHUB REPOSITORY##. It is separated into chunks that may be run independently by pressing the <em>play</em> button. To reproduce the reported results, all chunks have to be run from top to bottom. <strong>Several additional files</strong> are required in order to run the entire pipeline successfully:</p> +<p>This is an R Markdown file containing code to document the data analysis for the manuscript “DNA metabarcoding reveals broad presence of potentially plant pathogenic oomycetes in cross-border transported soil”. Results of the DADA2 analysis are parsed into phyloseq and other, generic data objects and metadata is added. In addition to documenting the analysis, the analysis should be fully reproducible using the provided files at the <a href="https://gitlab.nibio.no/simeon/oomycete-metabarcoding-supplementary">GitLab repository</a>. It is separated into chunks that may be run independently by pressing the <em>play</em> button. To reproduce the reported results, all chunks have to be run from top to bottom. <strong>Several additional files</strong> are required in order to run the entire pipeline successfully:</p> <ul> <li>A sequence table called <strong>‘seqtab_nochim.rds’</strong> (automatically generated by the DADA2 pipeline)</li> <li>A taxonomy table called <strong>‘taxa.rds’</strong> (automatically generated by the Dada2 pipeline)</li> @@ -1659,29 +1659,40 @@ summary { <div id="descriptor-table-and-initialization" class="section level4"> <h4>Descriptor table and initialization</h4> <p>The file ‘descriptors.txt’ is a tab-delimited .txt table containing metadata for all samples. It was generated using the chunk below (optional_sample_check = TRUE) and filled using the metadata from Supplementary table 1 but also contains data processing related columns that help adress certain data groups. This chunk points to the path of the input files and loads the necessary libraries. Warnings are disabled for this chunk as at least one function (dir.create) throws an expected warning every time the output path already exists. Set ‘warning=TRUE’ in the chunk header if you want to output warnings to the knitted document and output space under a given chunk (e.g. if you experience trouble loading certain libraries).</p> -<pre class="r"><code># CHANGE ME according to the location containing 'seqtab_nochim.rds' and other relevant files. +<pre class="r"><code># CHANGE ME according to the location containing 'seqtab_nochim.rds' and other +# relevant files. path = "Data_files/" # CHANGE ME to 'TRUE' to list all samples and generate an empty metadata file optional_sample_check <- "FALSE" -# CHANGE ME to TRUE to update the cuphyr package. This is a package the author published to separate out general purpose functions from this script. If the package is not installed, it will be installed, regardless of the value chosen here. +# CHANGE ME to TRUE to update the cuphyr package. This is a package the author +# published to separate out general purpose functions from this script. If the +# package is not installed, it will be installed, regardless of the value chosen +# here. update_cuphyr <- TRUE -################# NO CHANGES NECESSARY BELOW ################# -#global knitr settings to determine the output of the markdown document upon 'knitting' -knitr::opts_chunk$set(echo = FALSE) +################# NO CHANGES NECESSARY BELOW ################# + +# global knitr settings to determine the output of the markdown document upon +# 'knitting' +knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(root.dir = paste0(path)) -#Messages and warnings are turned off globally for a cleaner output. Change to TRUE for complete output + +# Messages and warnings are turned off globally for a cleaner output. +# Change to TRUE for complete output knitr::opts_chunk$set(message = FALSE) knitr::opts_chunk$set(warning = FALSE) +knitr::opts_chunk$set(tidy = FALSE) -#CuPhyR is the author's own package that contains several functions used to process phyloseq objects. It is hosted on GitHub and installed/updated using this command if update_cuphyr is TRUE +# CuPhyR is the author's own package that contains several functions used to +# process phyloseq objects. It is hosted on GitHub and installed/updated using +# this command if update_cuphyr is TRUE if (update_cuphyr | !"cuphyr" %in% rownames(installed.packages())) { devtools::install_github("simeross/cuphyr") } -#Loading required packages +# Loading required packages library(phyloseq) library(dada2) library(tidyverse) @@ -1700,10 +1711,11 @@ library(cuphyr) library(data.table) -#Defining output directory, this is where generated plots will be saved to. +# Defining output directory, this is where generated plots will be saved to. outp <- paste0(path,"/phyloseq_output") -#Checks whether output path exists and creates it if not. Throws warning if directory exists. +# Checks whether output path exists and creates it if not. Throws warning if +# directory exists. dir.create(file.path(outp)) if (optional_sample_check == "TRUE") { @@ -1711,17 +1723,18 @@ if (optional_sample_check == "TRUE") { samps <- rownames(seqtabcheck) lensamps <- length(samps) blankcol <- vector(mode = "character", length = lensamps) - blanktable <- data.frame(SampleIDs = samps, - ExampleProperty1 = blankcol, - ExampleProperty2 = blankcol, - ExampleProperty3 = blankcol) + blanktable <- data.frame(SampleIDs = samps, + ExampleProperty1 = blankcol, + ExampleProperty2 = blankcol, + ExampleProperty3 = blankcol) write.table(blanktable, file = paste0(path, "/descriptors_blank.txt"), sep = "\t", row.names = F) cat("'seqtab_nochim.rds' contains samples in the following order:\n", samps, "\nThe number of samples in the file is:", lensamps,sep = "\n") } -#Setting seed for reproducible results of randomized functions (mostly phylogenetic trees) +# Setting seed for reproducible results of randomized functions (mostly +# phylogenetic trees) set.seed(20301) #minmal message to confirm successful chunk pass @@ -1731,11 +1744,129 @@ cat("Chunk successfully run")</code></pre> <div id="parameters" class="section level4"> <h4>Parameters</h4> <p>This chunk allows the adjustment of several analysis parameters, such as <strong>setting the pruning of control samples</strong> based on keywords, <strong>requiring that a phylogenetic tree be provided or generated</strong>, <strong>defining a minimum ASV count</strong> and <strong>providing an alternative taxonomy</strong>.</p> +<pre class="r"><code>## CHANGE ME to "TRUE" to remove control samples from the analysis or "FALSE" to +# analyse all samples. +Prune_Controls <- "TRUE" + +## CHANGE ME to a list of unique identifiers that only occur in the names of +# control samples to classify. These samples will be separated into a dedicated +# phyloseq object to be analyzed apart from the core samples. Common examples +# are provided. +controls <- c("Pos", "H2O", "Neg", "Kontr", "Contr", "posK", "NTC", "POSK") + +## CHANGE ME to a number of ASV counts [~reads] that analyzed samples should +# minimally have. Samples with lower ASV counts than 'minASVcount' will be +# pruned. +minASVcount <- 5000 + +## CHANGE ME to "TRUE", if you want to provide a custom taxonomy table instead +# of using the default dada2 output ('taxa.rds'). +customTax <- "TRUE" + + ## CHANGE ME to the location of the custom taxonomy file. This only matters if + # customTax="TRUE", otherwise it will be ignored. + taxfile <- file.path(path, "custom_BLAST_taxonomy_nt.txt") + +## CHANGE ME to "TRUE" to generate a phylogenetic tree. This process takes a +# long time depending on the number of sequences (up to days for thousands). +# If a tree is provided as 'phylotree.rds' in 'path', then it will be used +# regardless of the value of 'maketree' +maketree <- "TRUE" + +## CHANGE ME to "TRUE" to root the used phylogenetic tree (if one exists) on the +# leaf with the longest branch (outgroup). This makes analyses that rely on the +# phylogenetic tree reproducible instead of picking a random leaf as root when +# calculating UNIFRAC distances. Implementation based on +# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in +# https://github.com/joey711/phyloseq/issues/597 +roottree <- "TRUE" + +# Determines whether a new version of the FUNGuild database should be downloaded +# and saved in 'path'. Otherwise uses existing RDS file in 'path'. If none +# exists, a new one will be downloaded regardless of the value of +# 'update_to_current'. +update_to_current <- "FALSE" + +#Setting a seed so functions that use randomization produce reproducible results +set.seed(4433) + +cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="parsing-input-data" class="section level4"> <h4>Parsing input data</h4> <p>This chunk parses the input data.<strong>This chunk does not require any user inputs</strong>. If no phylogenetic tree with the name ‘phylotree.rds’ was provided and ‘maketree <- “TRUE”’, it will be calculated here. The phylogenetic tree is necessary for certain plots that incorporate taxonomic relationships beyond the annotations, such as PCoA (not included here) or tree plots.</p> +<pre class="r"><code>### No changes necessary in this chunk + +##Read in variables +#Read in Sequence table +seqtabp <- readRDS(paste0(path,"/seqtab_nochim.rds")) +#Read in taxonomy (custom or RDP) +if (customTax == "TRUE") { + taxap <- read.delim(taxfile, header = TRUE, sep = "\t") + rownames(taxap) <- colnames(seqtabp) + taxap <- as.matrix(taxap) +}else{ + taxap <- readRDS(file.path(path,"taxa.rds"))} +#Read in metadata +samp_table <- read.delim(paste0(path, "/descriptors.txt"), header = TRUE, + sep = "\t") +#List samples from Sequence table +samp_list <- rownames(seqtabp) + + +# generate phylogenetic tree of ASVs only if there is no file called +# 'phylotree.rds' in the working directory and 'maketree' is "TRUE" +if (!file.exists(paste0(path, "/phylotree.rds"))) { + if (maketree == "TRUE") { + ASVs <- getSequences(seqtabp) + names(ASVs) <- ASVs + ASV_align <- alignment <- AlignSeqs(DNAStringSet(ASVs), anchor = NA) + ASV_phang <- phyDat(as(ASV_align, "matrix"), type = "DNA") + dm <- dist.ml(ASV_phang) + treeNJ <- NJ(dm) + fit = pml(treeNJ, data = ASV_phang) + fitGTR <- update(fit, k = 4, inv = 0.2) + fitGTR <- optim.pml(fitGTR, model = "GTR", optInv = TRUE, optGamma = TRUE, + rearrangement = "stochastic", + control = pml.control(trace = 0)) + saveRDS(fitGTR, file = paste0(path, "/phylotree.rds"))}} + +## parse into phyloseq object, either with or without phylogenetic tree +# information +row.names(samp_table) <- samp_list +if (file.exists(paste0(path, "/phylotree.rds"))) { + treep <- readRDS(paste0(path, "/phylotree.rds")) + p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), + sample_data(samp_table), tax_table(taxap), + phy_tree(treep$tree)) +}else{ + p <- phyloseq(otu_table(seqtabp, taxa_are_rows = FALSE), + sample_data(samp_table), tax_table(taxap))} + +##Adding nucleotide info and giving sequences ASV## identifiers +dna <- Biostrings::DNAStringSet(taxa_names(p)) +names(dna) <- taxa_names(p) +p <- merge_phyloseq(p, dna) +taxa_names(p) <- paste0("ASV", seq(ntaxa(p))) + +##optional pruning +if (Prune_Controls == "TRUE") { + if (!is.null(controls)) { + samp_clean <- samp_list[!samp_list %in% grep(paste0(controls, + collapse = "|"), + samp_list, value = T)] + contr_pruned <- setdiff(samp_list, samp_clean) + ps <- prune_samples(samp_clean, p) + cat("\n", + "Number of control samples that were pruned and will not be analysed:\n", + length(samp_list) - length(samp_clean), + "\n", + "The following controls were pruned:\n", + contr_pruned, "\n", sep = "\n") + }else{ + cat("\n\nPrune_Controls is TRUE but 'controls' is empty. No samples were pruned.\n\n")} +}else{ps <- p}</code></pre> <pre><code>## ## ## Number of control samples that were pruned and will not be analysed: @@ -1759,6 +1890,15 @@ cat("Chunk successfully run")</code></pre> ## OITS1-Pos3 ## OITS1-Pos4 ## OITS1-Pos5</code></pre> +<pre class="r"><code>if (minASVcount > 0) { + ps.raw <- ps + samp_pruned <- names(which(sample_sums(ps) < minASVcount)) + ps <- prune_samples(sample_sums(ps) >= minASVcount, ps) + if (length(samp_pruned) > 0) { + cat("The following samples were pruned because ASV counts were lower than", + minASVcount, ":\n") + cat(samp_pruned, "\n", sep = "\n")} + }</code></pre> <pre><code>## The following samples were pruned because ASV counts were lower than 5000 : ## 10A-OITS1 ## 12A-OITS1 @@ -1766,25 +1906,306 @@ cat("Chunk successfully run")</code></pre> ## 17B-OITS1 ## 18B-OITS1 ## 19B-OITS1</code></pre> +<pre class="r"><code>#Remove 0 count ASVs (e.g. control ASVs that remain) from the base object +ps <- prune_taxa(taxa_sums(ps) > 0, ps) +ps</code></pre> <pre><code>## phyloseq-class experiment-level object ## otu_table() OTU Table: [ 5195 taxa and 122 samples ] ## sample_data() Sample Data: [ 122 samples by 9 sample variables ] ## tax_table() Taxonomy Table: [ 5195 taxa by 7 taxonomic ranks ] ## phy_tree() Phylogenetic Tree: [ 5195 tips and 5193 internal nodes ] ## refseq() DNAStringSet: [ 5195 reference sequences ]</code></pre> +<pre class="r"><code>cat("","Summary of sample counts", + "Average ASV count per sample (incl. controls and unfiltered):", + mean(phyloseq::sample_sums(p)), + "Average ASV count per sample after pruning controls and filtering low count samples:", + mean(phyloseq::sample_sums(ps)), sep = "\n")</code></pre> <pre><code>## ## Summary of sample counts ## Average ASV count per sample (incl. controls and unfiltered): ## 35056.64 ## Average ASV count per sample after pruning controls and filtering low count samples: ## 33716.98</code></pre> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="functions" class="section level4"> <h4>Functions</h4> <p>This chunk defines various custom functions that are used in chunks below. <strong>This chunk is absolutely necessary for downstream code to work.</strong></p> +<pre class="r"><code>################################################ + +# This funtion defines the leaf with the longest path as the root of the +# phylogenetic tree. This makes results reproducible by avoiding the behaviour +# of some functions that would otherwise pick a random leaf as the root of an +# unrooted phylogenetic tree. Based on answers in +# https://github.com/joey711/phyloseq/issues/597 +pick_new_outgroup <- function(tree.unrooted){ + require("magrittr") + require("data.table") + require("ape") # for ape::Ntip + treeDT <- + cbind( + data.table(tree.unrooted$edge), + data.table(length = tree.unrooted$edge.length) + )[1:Ntip(tree.unrooted)] %>% + cbind(data.table(id = tree.unrooted$tip.label)) + # longest terminal branch as outgroup + new.outgroup <- treeDT[which.max(length)]$id + return(new.outgroup) } + +################################################ + +# Generic save function for plots that checks whether file exists and if so, +# creates a new one with d/m/y+time info to avoid overwriting. +save_plot <- function(pl, filetype = ".png", plot_name = "my_plot"){ +name <- paste0("/",plot_name,filetype) +if (file.exists(paste0(outp, name))) { +name <- paste0("/", plot_name, "_", format(Sys.time(), "%d-%m-%y_%H%M%S"),filetype)} +ggsave(paste0(outp, name), pl, width = wp, height = hp, unit = "cm", dpi = res) +} + +################################################ + +# Function to download and parse the FUNGuild, adapted from S. Faye Smith (2018) +# in https://rpubs.com/faysmith/metabarcoding +parse_funguild <- function(url = 'http://www.stbates.org/funguild_db.php', + tax_name = TRUE){ + require(XML) + require(jsonlite) + require(RCurl) + ## Parse data + tmp <- XML::htmlParse(url) + tmp <- XML::xpathSApply(doc = tmp, path = "//body", fun = XML::xmlValue) + ## Read url and convert to data.frame + db <- jsonlite::fromJSON(txt = tmp) + ## Remove IDs + db$`_id` <- NULL + if (tax_name == TRUE) { + ## Code legend + ## Taxon Level: A numeral corresponding the correct taxonomic level for the + # taxon + taxons <- c( + "keyword", # 0 + "Phylum", "Subphylum", "Class", "Subclass", "Order", "Suborder", # 3:8 + "Family", "Subfamily", "Tribe", "Subtribe", "Genus", # 9:13 + "Subgenus", "Section", "Subsection", "Series", "Subseries", # 15:19 + "Species", "Subspecies", "Variety", "Subvariety", "Form", # 20:24 + "Subform", "Form Species") + ## Table with coding + taxmatch <- data.frame( + TaxID = c(0, 3:13, 15:26), + Taxon = factor(taxons, levels = taxons)) + ## Match taxon codes + db$taxonomicLevel <- taxmatch[match(x = db$taxonomicLevel, + table = taxmatch$TaxID), "Taxon"] + } + ## Add database dump date as attributes to the result + attr(db, "DownloadDate") <- date() + db <- as_tibble(db) + return(db) +} + + +cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> <p>This chunk calculates most phyloseq objects, vectors, lists and tables that are used to generate figures and other output below. This somewhat clutters the global environment but keeps the data available and makes the chunks that generate output more efficient. <strong>This chunk is absolutely necessary for downstream code to work.</strong></p> +<pre class="r"><code># Get a tbl of the base object for easier access in some phyloseq-independent +# analyses +ps_tbl <- as_tibble(psmelt(ps)) + +# Transformed +ps.trans <- transform_sample_counts(ps, function(ASV) ASV/sum(ASV)) + +# Just controls +ps.contr <- subset_samples(p, Comment == "Control") +ps.contr <- prune_taxa(taxa_sums(ps.contr) >= 20, ps.contr) +ps.transcont <- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV)) + +# FUNGuild update check and/or load +if (update_to_current == "TRUE" | !file.exists(paste0(path, + "/FUNGuild-db.rds"))) { + FUNGuild <- parse_funguild() + saveRDS(FUNGuild, file = paste0(path, "/FUNGuild-db.rds")) +}else{ + FUNGuild <- readRDS(paste0(path, "/FUNGuild-db.rds")) +} +FUNGuild_tbl <- select(FUNGuild, taxon, trophicMode:growthForm) + +# Generate a ps_tbl subset for oomycetes +ps_tbl_Oomycs <- filter(ps_tbl, str_detect(Class, 'Oomycetes')) %>% + select(OTU, Abundance, Kingdom:Species) %>% + mutate(Species = gsub("_", " ", Species)) %>% + group_by(OTU) +# Make a tbl of all oomycete ASVs and their classificaiton according to FUNGuild +ASVs_funguild <- summarise(ps_tbl_Oomycs, Abundance_sum = sum(Abundance)) %>% + left_join(ps_tbl_Oomycs, by = "OTU") %>% + select(-Abundance) %>% + distinct() %>% + left_join(FUNGuild_tbl, by = c("Genus" = "taxon")) %>% + left_join(FUNGuild_tbl, by = c("Species" = "taxon")) %>% + dplyr::rename(trophic_genus = trophicMode.x, + guild_genus = guild.x, + trophic_species = trophicMode.y, + guild_species = guild.y) %>% + arrange(Genus) %>% + unite(Plant_pathogenic, c(guild_genus, guild_species), + remove = FALSE) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "Plant Pathogen_Plant Pathogen", "++")) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "Plant Pathogen_NA", "+")) %>% + mutate(Plant_pathogenic = str_replace( + Plant_pathogenic, "NA_NA", "-")) #%>% + +# Guild status of all oomyc genera and subets of pp genera and other genera +genus_list <- ASVs_funguild %>% + dplyr::group_by(Genus, guild_genus) %>% + summarise() + +genera_pp <- filter(genus_list, guild_genus == "Plant Pathogen") %>% + select(Genus) %>% + unlist() %>% + unname() + +genera_other <- filter(genus_list, + guild_genus != "Plant Pathogen" | is.na(guild_genus)) %>% + select(Genus) %>% + unlist() %>% + unname() + +# Subset by vector of taxonomy +## CHANGE me to a vector containing taxonomic terms to subset on +subVector <- c("Oomycetes") +## CHANGE ME to the taxonomic level at which you want to search for an entry +subTax <- "Class" + +subASVs <- cuphyr::list_subset_ASVs(subv = subVector, taxlvlsub = subTax) +ps.subs <- prune_taxa(subASVs, ps) +ps.transsub <- prune_taxa(subASVs, ps.trans) + +# Subset by sample descriptor +ps.bait <- subset_samples(ps, Bait == "Enriched") +ps.untr <- subset_samples(ps, Bait == "Before enrichment") +samps.bait <- sample_names(ps.bait) +samps.untr <- sample_names(ps.untr) +ps.bait <- prune_samples(samps.bait, ps) +ps.untr <- prune_samples(samps.untr, ps) +ps.bait.trans <- prune_samples(samps.bait, ps.trans) +ps.untr.trans <- prune_samples(samps.untr, ps.trans) + +# Create subset lists +subASVsAlg <- cuphyr::list_subset_ASVs(subv = c("Chrysophyceae"), + taxlvlsub = "Class") +subASVsOom <- cuphyr::list_subset_ASVs(subv = c("Oomycetes"), + taxlvlsub = "Class") +subASVsPP <- cuphyr::list_subset_ASVs(subv = c(genera_pp), + taxlvlsub = "Genus") +subASVs_uncultured <- cuphyr::list_subset_ASVs(subv = c("uncultured"), + taxlvlsub = "Genus") +# remove 'uncultured' entries from the PP ASVs, since these should not be +# counted as PP. +subASVsPP <- setdiff(subASVsPP, subASVs_uncultured) + +# Taxonomic subset on sample descriptor subset +ps.b.tr.Alg <- prune_taxa(subASVsAlg, ps.bait.trans) +ps.u.tr.Alg <- prune_taxa(subASVsAlg, ps.untr.trans) +ps.b.Alg <- prune_taxa(subASVsAlg, ps.bait) +ps.u.Alg <- prune_taxa(subASVsAlg, ps.untr) +ps.b.tr.Oom <- prune_taxa(subASVsOom, ps.bait.trans) +ps.u.tr.Oom <- prune_taxa(subASVsOom, ps.untr.trans) +ps.b.tr.PP <- prune_taxa(subASVsPP, ps.bait.trans) +ps.u.tr.PP <- prune_taxa(subASVsPP, ps.untr.trans) +ps.b.PP <- prune_taxa(subASVsPP, ps.bait) +ps.u.PP <- prune_taxa(subASVsPP, ps.untr) + +# Rank some ps objects +ranksBaitAlgae <- cuphyr::make_ranked_sums(ps.b.tr.Alg, myset = "Chrysophyceae") +ranksUntrAlgae <- cuphyr::make_ranked_sums(ps.u.tr.Alg, myset = "Chrysophyceae") +ranksBaitOom <- cuphyr::make_ranked_sums(ps.b.tr.Oom, myset = "All Oomycetes") +ranksUntrOom <- cuphyr::make_ranked_sums(ps.u.tr.Oom, myset = "All Oomycetes") +ranksBaitPP <- cuphyr::make_ranked_sums(ps.b.tr.PP, myset = "PP") +ranksUntrPP <- cuphyr::make_ranked_sums(ps.u.tr.PP, myset = "PP") + +ranksBaitAlgae_tot <- cuphyr::make_ranked_sums(ps.b.Alg, + myset = "Chrysophyceae_tot") +ranksUntrAlgae_tot <- cuphyr::make_ranked_sums(ps.u.Alg, + myset = "Chrysophyceae_tot") +ranksBaitPP_tot <- cuphyr::make_ranked_sums(ps.b.PP, myset = "PP_tot") +ranksUntrPP_tot <- cuphyr::make_ranked_sums(ps.u.PP, myset = "PP_tot") + +### rerank tables for combined figures +OomRankPP <- ranksBaitOom +# Adopt rank number from PP so that the sample order is the same +OomRankPP$Rank <- ranksBaitPP$Rank +OomRankPP$Set <- "Other Oomycetes" +# Analogous to above +UntrOomRankPP <- ranksUntrOom +UntrOomRankPP$Rank <- ranksUntrPP$Rank +UntrOomRankPP$Set <- "Other Oomycetes" +AlgaeInvRankOom <- ranksBaitAlgae +AlgaeInvRankOom$Rank <- ranksBaitPP$Rank +# Add ASV counts so that Chrysophyceae appear above the total Oomycete bars in +# the overlay +AlgaeInvRankOom$Abundance <- AlgaeInvRankOom$Abundance + OomRankPP$Abundance +UntrAlgaeInvRankOom <- ranksUntrAlgae +UntrAlgaeInvRankOom$Rank <- ranksUntrPP$Rank +UntrAlgaeInvRankOom$Abundance <- UntrAlgaeInvRankOom$Abundance + + UntrOomRankPP$Abundance +# Oom without PP +ranksBaitOwoP <- OomRankPP +ranksBaitOwoP$Set <- "Other Oomycetes" +ranksBaitOwoP$Abundance <- ranksBaitOwoP$Abundance - ranksBaitPP$Abundance +ranksBaitOwoP <- ranksBaitOwoP[order(-ranksBaitOwoP$Abundance),] +ranksBaitOwoP$Rank <- c(1:nrow(ranksBaitOwoP)) +ranksUntrOwoP <- UntrOomRankPP +ranksUntrOwoP$Set <- "Other Oomycetes" +ranksUntrOwoP$Abundance <- ranksUntrOwoP$Abundance - ranksUntrPP$Abundance +ranksUntrOwoP <- ranksUntrOwoP[order(-ranksUntrOwoP$Abundance),] +ranksUntrOwoP$Rank <- c(1:nrow(ranksUntrOwoP)) +# Make dummy tables for 'other' ASV counts that fills up the sample bars to 1 +# and contains the total ASV count for ASVs other than Oomycetes and +# Chrysophyceae in the column 'OtherASVcounts' +OtherASVcounts <- 1 - AlgaeInvRankOom$Abundance +OthersRankPP <- cbind(AlgaeInvRankOom, OtherASVcounts) +OthersRankPP$Abundance <- 1 +OthersRankPP$Set <- "Other classes" +# Rank Other ASVs for untreated and enriched samples to display in B and C +# respectively +rankedOthersBait <- OthersRankPP[order(-OthersRankPP$OtherASVcounts),] +rankedOthersBait$Rank <- c(1:nrow(rankedOthersBait)) +# Analogous to above +OtherASVcounts <- 1 - UntrAlgaeInvRankOom$Abundance +UntrOthersRankPP <- cbind(UntrAlgaeInvRankOom, OtherASVcounts) +UntrOthersRankPP$Abundance <- 1 +UntrOthersRankPP$Set <- "Other classes" +rankedOthersUntr <- UntrOthersRankPP[order(-UntrOthersRankPP$OtherASVcounts),] +rankedOthersUntr$Rank <- c(1:nrow(rankedOthersUntr)) + +if (roottree == "TRUE") { + my.tree <- phy_tree(ps) + out.group <- pick_new_outgroup(my.tree) + new.tree <- ape::root(my.tree, outgroup = out.group, resolve.root = TRUE) + phy_tree(ps) <- new.tree} + +# export ps in clean table format for Supplementary Material +ps_tbl_wide <- ps_tbl %>% + arrange(Soil, Bait) %>% + pivot_wider(id_cols = c(OTU, Kingdom, Phylum, + Class, Order, Family, + Genus, Species), + names_from = c(Soil, Bait), values_from = Abundance) +ps_tbl_export <- ps_tbl_wide %>% + mutate(num_ASV = as.numeric(str_remove(OTU, "ASV"))) %>% + arrange(num_ASV) %>% select(-num_ASV) %>% + dplyr::rename(ASV = OTU) +# export as csv +write_csv(ps_tbl_export, path = file.path(path, "Supplementary_table_2.csv")) + +# Optional: export as xlsx, requires 'writexl' package +#writexl::write_xlsx(ps_tbl_export, path = file.path(path, +# "Supplementary table 2.xlsx")) + +cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> </div> @@ -1794,21 +2215,109 @@ cat("Chunk successfully run")</code></pre> <div id="plot-looks" class="section level4"> <h4>Plot looks</h4> <p>This chunk sets the background structure and color palette. Viridis was chosen because it is optimized for grey-scale printing and various types of color blindness and More info on the Viridis palette can be found on <a href="https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html">the Viridis info page</a>.</p> +<pre class="r"><code>## CHANGE ME to preferred ggplot2 theme. Recommended: "theme_bw()". +theme_set(theme_bw()) + +my_scale_col <- scale_color_viridis(discrete = TRUE) +my_scale_fill <- scale_fill_viridis(discrete = TRUE) + +# Custom, more narrow color ranges based on viridis +# Base order to have adjacent colors be distinct from each other +sort_colors <- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20))) + +# Customized vectors +n_col <- 20 +viridis_greens <- viridis(n_col, option = "D", + begin = 0.85 ,end = 0.7)[sort_colors] +viridis_reds <- viridis(n_col, option = "B", + begin = 0.7 ,end = 0.5)[sort_colors] +viridis_blues <- viridis(n_col, option = "D", + begin = 0.2 ,end = 0.4)[sort_colors] +viridis_yellows <- viridis(n_col, option = "D", + begin = 1 ,end = 0.9)[sort_colors] +viridis_dark <- viridis(n_col, option = "A", + begin = 0 ,end = 0.1)[sort_colors] +viridis_light <- viridis(n_col, option = "A", + begin = 1 ,end = 0.9)[sort_colors] +sub_viridis <- list(viridis_greens, viridis_blues, viridis_yellows, + viridis_light, viridis_reds, viridis_dark) + +cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="positive-controls" class="section level4"> <h4>Positive controls</h4> <p>This chunk generates an overview over the positive controls (Supplementary Fig. 2)</p> +<pre class="r"><code>## CHANGE ME to the secondary parameter of interest (categories on the x-axis). +# Accepted values are the column headers in your descriptor file. +comboPar <- "Soil" + +# CHANGE ME to the primary parameter of interest (separate panels). +# Accepted values are the column headers in your descriptor file. +treePar <- "Run" + +## CHANGE ME to the taxonomic level of interest (color coding). +# Accepted values are the column headers in your descriptor file. +taxlvltre <- "OTU" +taxlvlbar <- "OTU" + +##CHANGE ME to change the width (in cm) of the output. +wp <- 19 +#CHANGE ME to change the height (in cm) of the output. +hp <- 22 +#CHANGE ME to change the resolution (in dpi) of the output. +res <- 300 + +topnpplot <- plot_bar(ps.contr, x = comboPar, fill = taxlvlbar) + + scale_fill_viridis_d() + + theme(axis.title.x = element_blank(), legend.position = "none", + legend.key.size = unit(3, "mm")) + ylab("ASV counts") + + guides(col = guide_legend(ncol = 3)) + labs(fill = "ASV") + +topntplot <- plot_bar(ps.transcont, x = comboPar, fill = taxlvlbar) + + scale_fill_viridis_d() + + theme(axis.title.x = element_blank(), + legend.position = "none", legend.key.size = unit(3, "mm")) + + ylab("Relative abundance") + + guides(col = guide_legend(ncol = 3)) + + labs(fill = "ASV") + +tre <- plot_tree(ps.transcont, ladderize = "left", + label.tips = taxlvltre, color = "abundance", + text.size = 2.5, shape = "Run") + + scale_color_viridis_c(aesthetics = c("color","fill")) + + theme(legend.position = "left", panel.border = element_blank()) + +combocontr <- ggarrange(tre, ggarrange(topnpplot, topntplot, + ncol = 2, labels = c("B", "C"), + align = "hv", common.legend = TRUE, + legend = "right"), nrow = 2, + legend = "right", labels = c("A")) + +save_plot(combocontr, plot_name = "Barplot_controls_combined_supfig2") + +combocontr</code></pre> <p><img src="data:image/png;base64,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width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="phylogenetic-tree-of-positive-controls" class="section level4"> <h4>Phylogenetic tree of positive controls</h4> <p>This chunk generate a phylogenetic tree of the positive control sequences and ASVs detected in the positive controls.</p> +<pre class="r"><code>## CHANGE ME to change the width (in cm) of the output. +wp <- 20 +# CHANGE ME to change the height (in cm) of the output. +hp <- 15 + +real_contr <- refseq(ps.contr) +seqs <- readDNAStringSet(file = "Data_files/Pos_contrseqs.fasta", + format = "fasta") +seqs <- DNAStringSet(c(real_contr, seqs)) +align <- AlignSeqs(DNAStringSet(seqs), anchor = NA)</code></pre> <pre><code>## Determining distance matrix based on shared 9-mers: ## ================================================================================ ## -## Time difference of 0.02 secs +## Time difference of 0.04 secs ## ## Clustering into groups by similarity: ## ================================================================================ @@ -1818,7 +2327,7 @@ cat("Chunk successfully run")</code></pre> ## Aligning Sequences: ## ================================================================================ ## -## Time difference of 1.77 secs +## Time difference of 2.18 secs ## ## Iteration 1 of 2: ## @@ -1830,12 +2339,12 @@ cat("Chunk successfully run")</code></pre> ## Reclustering into groups by similarity: ## ================================================================================ ## -## Time difference of 0.03 secs +## Time difference of 0.05 secs ## ## Realigning Sequences: ## ================================================================================ ## -## Time difference of 0.76 secs +## Time difference of 1.21 secs ## ## Iteration 2 of 2: ## @@ -1852,26 +2361,158 @@ cat("Chunk successfully run")</code></pre> ## Realigning Sequences: ## ================================================================================ ## -## Time difference of 0.04 secs +## Time difference of 0.03 secs ## ## Refining the alignment: ## ================================================================================ ## ## Time difference of 0.27 secs</code></pre> +<pre class="r"><code>seqs_phang <- phangorn::phyDat(as(align, "matrix"), type = "DNA") +seqs_dm <- phangorn::dist.ml(seqs_phang) +seqs_treeNJ <- NJ(seqs_dm) +seqs_fit = pml(seqs_treeNJ, data = seqs_phang) +fitGTRseqs <- update(seqs_fit, k = 4, inv = 0.2) +fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, + optGamma = TRUE, + rearrangement = "stochastic", + control = pml.control(trace = 0)) + +out.group <- pick_new_outgroup(fitGTRseqs$tree) +new.tree <- ape::root(fitGTRseqs$tree, outgroup = out.group, + resolve.root = TRUE) +fitGTRseqs_outgroup <- new.tree + +# GTR_tree <- treeio::as.treedata(fitGTRseqs, type="ml") +kontr_ASV_tree <- ggtree(fitGTRseqs_outgroup) + + geom_tree() + geom_treescale(x = 1) + geom_tiplab() + xlim(0,4) + +save_plot(kontr_ASV_tree, plot_name = "Pos-ASVs_kontr-seqs_tree") +kontr_ASV_tree</code></pre> <p><img 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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="data-overview" class="section level4"> <h4>Data overview</h4> <p>This chunk generates an overview over read counts for all samples and additional info on the data (Supplementary Fig. 1). ASV counts are summed up for all samples in the provided phyloseq object and assigned a rank based on this sum. The sample with the highest amount of total ASV counts will receive rank 1, the one with the second highest rank 2 and so on. A bar plot is generated, plotting the total ASV counts against the sample rank with color filling by run and a dashed line indicating the chosen cutoff defined in minASVcount (Parameter chunk).</p> +<pre class="r"><code>## CHANGE ME to change the width (in cm) of the output. +wp = 20 +# CHANGE ME to change the height (in cm) of the output. +hp = 15 +# CHANGE ME to change the resolution (in dpi) of the output. +res = 300 + +# Rank all samples, including those that were discarded from 'ps', +# but excluding controls +all <- cuphyr::make_ranked_sums(ps.raw, myset = "Total") +# This calculates an average read count for all samples that can be added to +# the plot as e.g. a dashed line or text +avg <- mean(all$Abundance) +pall <- ggplot(data = all, aes(y = Abundance, x = Samples)) + +# plot figure +overview <- ggplot(data = all, aes(x = Rank, y = Abundance, fill = Run)) + + geom_col() + my_scale_fill + + geom_hline(yintercept = minASVcount, linetype = "dashed") + + ylab("Total ASV counts ('reads')") + +# Also give the number of ASVs, oomycete ASVs and calculate the percentage of +# ASVs identified as oomycetes +oomlist <- cuphyr::list_subset_ASVs(subv = c("Oomycetes"), taxlvlsub = "Class") + +# Save figure +save_plot(overview, plot_name = "Overview_SupFig1") + +overview</code></pre> <p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>cuphyr::summarise_physeq(physeq = ps, + ASV_sublist = oomlist, + sublist_id = "Oomycetes", samp_names = FALSE)</code></pre> <pre><code>## There are 5195 ASVs in the phyloseq object 'ps'. ## Of this, 1832 belong to the provided subset (Oomycetes), representing 71.84 percent of abundance per sample on average.</code></pre> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="top-n-asvstaxa-bar-plot" class="section level4"> <h4>Top N ASVs/taxa Bar plot</h4> <p>This chunk plots abundance of the Top n ASVs and taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. In the manuscript, the top 5 classes and top 10 genera are shown in Fig. 1.</p> +<pre class="r"><code># CHANGE ME to the amount of top taxlvl to plot (e.g. 'numt = 20' plots Top20 +# Taxa at taxlvl) +numt <- 5 + +# CHANGE ME to the amount of top taxa you want to plot (e.g. 'numt2 = 25' plots +# Top25 Taxa at taxlvl2) +numt2 <- 10 + +# CHANGE ME to the primary parameter of interest (x-axis). Accepted values are +# the column headers in the descriptor file. +primaryPar <- "Soil" + +## CHANGE ME to the secondary parameter of interest (panels). Accepted values +# are the column headers in the descriptor file. +secondaryPar <- "Bait" + +## CHANGE ME to the taxonomic level of interest (color coding). Accepted values +# are available taxonomic levels. 'taxlvl' should be higher than 'taxlvl2', e.g. +# Class and Genus +taxlvl <- "Class" +taxlvl2 <- "Genus" + +# Adds formatted legend labels for the specific output in the manuscript. +# WARNING! Will overwrite actual values and needs to be turned off/adjusted +# manually when data is updated +customLabels <- TRUE + +## CHANGE ME to change the width (in cm) of the output. +wp <- 20 +# CHANGE ME to change the height (in cm) of the output. +hp <- 15 +# CHANGE ME to change the resolution (in dpi) of the output. +res <- 300 + +# Get total number of reads and number of Pythium/Phytophthora reads (without +# control samples!) to find out percentage of Pyhium and Phytophthora +total_reads_no_contr <- ps_tbl %>% + dplyr::filter(Pass == "Yes") %>% + dplyr::mutate(sumAbu = sum(Abundance)) %>% + select(sumAbu) %>% + unique() %>% + unlist() %>% + unname() + +pythium_reads_no_contr <- ps_tbl %>% + dplyr::filter(Pass == "Yes") %>% + dplyr::group_by(Genus) %>% + summarise(sumAbu = sum(Abundance)) %>% + dplyr::filter(Genus == "Pythium") %>% + select(sumAbu) %>% + unlist() %>% + unname() + +phytoph_reads_no_contr <- ps_tbl %>% + dplyr::filter(Pass == "Yes") %>% + dplyr::group_by(Genus) %>% + summarise(sumAbu = sum(Abundance)) %>% + dplyr::filter(Genus == "Phytophthora") %>% + select(sumAbu) %>% + unlist() %>% + unname() + +prctg_pyth_phyt_str <- paste0("\n\nTotal number of reads in the samples: ", + total_reads_no_contr , + ", number of Pythium reads: ", + pythium_reads_no_contr, + ", number of Phytophthora reads: ", + phytoph_reads_no_contr, + "\nPercentage of Pythium total: ", + pythium_reads_no_contr/total_reads_no_contr*100, + "%\nPercentage of Phytophthora total: ", + phytoph_reads_no_contr/total_reads_no_contr*100, "%") + +# Most abundant at specified levels, phyloseq objects +ps.topnt <- cuphyr::abundant_tax_physeq( + physeq = ps.trans, lvl = taxlvl, top = numt, + ignore_na = TRUE, silent = FALSE)</code></pre> <pre><code>## ## The top 5 most abundant annotated groups at the taxonomic level 'Class' are: ## Oomycetes @@ -1879,6 +2520,9 @@ cat("Chunk successfully run")</code></pre> ## 'uncultured fungus' ## Bacillariophyceae ## Dinophyceae</code></pre> +<pre class="r"><code>ps.topnt2 <- cuphyr::abundant_tax_physeq( + physeq = ps.trans,lvl = taxlvl2, top = numt2, + ignore_na = TRUE, silent = FALSE)</code></pre> <pre><code>## ## The top 10 most abundant annotated groups at the taxonomic level 'Genus' are: ## Pythium @@ -1891,36 +2535,429 @@ cat("Chunk successfully run")</code></pre> ## Haptoglossa ## Aphanomyces ## Pythiopsis</code></pre> +<pre class="r"><code># Abundance lists (sorted by abundance) +taxlist_topnt1 <- cuphyr::abundant_tax_physeq( + physeq = ps.trans, lvl = taxlvl, top = numt, + output_format = "tops", ignore_na = TRUE) +taxlist_topnt2 <- cuphyr::abundant_tax_physeq( + physeq = ps.trans, lvl = taxlvl2, top = numt2, + output_format = "tops", ignore_na = TRUE) + +# tidy evaluation translation magic (needed so the dplyr functions that extract +# 'tax_lookup' below can parse the strings from the variable) +lvl1 <- sym(taxlvl) +lvl2 <- sym(taxlvl2) +lvl1 <- enquo(lvl1) +lvl2 <- enquo(lvl2) + +#make lookup table to guide coloring +tax_lookup <- ps_tbl %>% + group_by(!!lvl1, !!lvl2) %>% + summarise() %>% + filter(!!lvl2 %in% taxlist_topnt2) + +# Define fixed color schemes for classes for consistency between plots. This is +# a color scheme specified for this particular published figure. Can be used for +# orientation in general cases. +# Background colors to be overwritten for special cases +colors_lvl1 <- viridis(length(taxlist_topnt1), + option = "D", begin = 1 , end = 0.5) +names(colors_lvl1) <- taxlist_topnt1 +colors_lvl2 <- viridis(length(taxlist_topnt2), + option = "D", begin = 1 , end = 0.5) +names(colors_lvl2) <- taxlist_topnt2 +colors_combo <- c(colors_lvl1, colors_lvl2) + +#Special cases +colors_combo["Chrysophyceae"] <- viridis_yellows[2] +colors_combo["Pedospumella"] <- viridis_yellows[1] +colors_combo["'Spumella-like'"] <- viridis_light[1] + +colors_combo["Oomycetes"] <- viridis_reds[4] +colors_combo["Phytophthora"] <- viridis_reds[1] +colors_combo["Pythium"] <- viridis_reds[4] +colors_combo["Aphanomyces"] <- viridis_blues[1] +colors_combo["Globisporangium"] <- "#4C0000FF" +colors_combo["Pythiogeton"] <- viridis_blues[2] +colors_combo["Saprolegnia"] <- viridis_blues[4] + +#Format legend labels +if(customLabels){ + labelling_lvl2 <- c(expression(paste(italic("Spumella"), "-like")), + "uncultured fungus", expression(italic("Aphanomyces"), + italic("Globisporangium"), italic("Haptoglossa"), italic("Pedospumella"), + italic("Phytophthora"), italic("Pythiogeton"), italic("Pythiopsis"), + italic("Pythium"))) +}else{ + labelling_lvl2 <- names(colors_lvl2) %>% sort() +} + +#Re-label the facets +enrich_labs <- c("Before enrichment", "After enrichment") +names(enrich_labs) <- c("Before enrichment", "Enriched") + +theme_bar <- theme(legend.position = "bottom", + legend.key.size = unit(0.4, "cm"), + legend.spacing.x = unit(0.3, 'cm'), + text = element_text(size = 10), + strip.text.x = element_text(size = 10), + strip.background = element_blank(), + axis.text.x = element_text(angle = 0, hjust = 0.5), + legend.text.align = 0) + +topnt <- plot_bar(ps.topnt, x = primaryPar, fill = taxlvl, + title = paste("Top", numt, "Classes")) + + facet_wrap(paste0("~", secondaryPar), + labeller = labeller(Bait = enrich_labs)) + + scale_x_discrete(breaks = c("S05" ,"S15", "S25", "S35", "S45" ,"S55")) + + scale_fill_manual(values = colors_combo) + + ylab("Relative abundance") + + theme_bar + + xlab("Soil samples S01-S64") + +topnt_summary <- cuphyr::summarise_physeq(physeq = ps, + ASV_sublist = taxa_names(ps.topnt), + sublist_id = + paste0("top ", numt, " ", taxlvl), + samp_names = FALSE)</code></pre> <pre><code>## There are 5195 ASVs in the phyloseq object 'ps'. ## Of this, 3706 belong to the provided subset (top 5 Class), representing 96.10 percent of abundance per sample on average.</code></pre> +<pre class="r"><code>topnt2 <- plot_bar(ps.topnt2, x = primaryPar, fill = taxlvl2, + title = paste("Top", numt2, "Genera")) + + facet_wrap(paste0("~", secondaryPar), + labeller = labeller(Bait = enrich_labs)) + + scale_x_discrete(breaks = c("S05" ,"S15", "S25", "S35", "S45" ,"S55")) + + scale_fill_manual(values = colors_combo, labels = labelling_lvl2) + + ylab("Relative abundance") + + theme_bar + xlab("Soil samples S01-S64") +topnt2_summary <- cuphyr::summarise_physeq(physeq = ps, + ASV_sublist = taxa_names(ps.topnt2), + sublist_id = + paste0("top ", numt2, " ", taxlvl2), + samp_names = FALSE)</code></pre> <pre><code>## There are 5195 ASVs in the phyloseq object 'ps'. ## Of this, 2649 belong to the provided subset (top 10 Genus), representing 86.63 percent of abundance per sample on average.</code></pre> +<pre class="r"><code>#Combine the plots +combobar1 <- ggarrange(topnt, topnt2, nrow = 2, + labels = c("A", "B"), align = "hv") + +#Save +save_plot(combobar1, plot_name = "Topn_barplot_Fig1") + +#Print to standard out +cat(topnt_summary, "\n\n", topnt2_summary)</code></pre> +<pre class="r"><code>cat(prctg_pyth_phyt_str)</code></pre> <pre><code>## ## ## Total number of reads in the samples: 4113471, number of Pythium reads: 2003562, number of Phytophthora reads: 263367 ## Percentage of Pythium total: 48.7073325665843% ## Percentage of Phytophthora total: 6.40254908810588%</code></pre> +<pre class="r"><code>combobar1</code></pre> <p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="functional-classification-using-the-funguild-database" class="section level4"> <h4>Functional classification using the FUNGuild database</h4> -<p>This chunk imports the FUNGuild database based on code by <a href="https://rpubs.com/faysmith/metabarcoding">S. Faye Smith (2018)</a>, then classifies and plots all oomycete genera in the data set (Fig. 2). <img src="data:image/png;base64,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" width="672" /></p> +<p>This chunk imports the FUNGuild database based on code by <a href="https://rpubs.com/faysmith/metabarcoding">S. Faye Smith (2018)</a>, then classifies and plots all oomycete genera in the data set (Fig. 2).</p> +<pre class="r"><code># Plot save dimensions +wp <- 20 +hp <- 15 +res <- 300 + +# Adds formatted legend labels for the specific output in the manuscript. +# WARNING! Will overwrite actual values and needs to be turned off/adjusted +# manually when data is updated +customLabels <- TRUE + +# Custom color coding to emphasize pp genera, particularly Pythium and +# Phytophthora +colors_genera_pp <- viridis(length(genera_pp), option = "D", + begin = 0 , end = 0.8) +names(colors_genera_pp) <- genera_pp +colors_genera_other <- viridis(length(genera_other), option = "D", + begin = 1 , end = 0.9) +names(colors_genera_other) <- genera_other +colors_oomyc_genera <- c(colors_genera_pp, colors_genera_other) + +# Sort alphabetically by vector names to avoid mixing up color assignments +# (origin of issue not resolved) +colors_oomyc_genera <- colors_oomyc_genera[order(names(colors_oomyc_genera))] +colors_oomyc_genera["Phytophthora"] <- viridis_reds[1] +colors_oomyc_genera["Pythium"] <- viridis_reds[4] + +#define ggplot theme (shared between genus and species level plots) +shared_theme <- theme(legend.key.size = unit(3, "mm"), + legend.position = "none", + axis.title.x = element_blank(), + plot.margin = margin(20,10,10,10), + legend.text.align = 0) + +shared_theme_abu <- theme(legend.key.size = unit(3, "mm"), + legend.position = "none", + axis.title.x = element_blank(), + plot.margin = margin(20,10,10,10), + legend.text.align = 0) + +#Format legend labels +if(customLabels){ + labelling <- c(expression(paste("uncultured ", italic("Apodachlya"))), + expression(paste("uncultured ", italic("Lagenidium"))), + expression(paste("uncultured ", italic("Oomycetes"))), + expression(paste("uncultured ", italic("Phytophthora"))), + expression(paste("uncultured ", italic("Pythium"))), + expression(italic("Achlya"), italic("Albugo"), italic("Aphanomyces"), + italic("Apodachlya"), italic("Atkinsiella"), italic("Bremia"), + italic("Brevilegnia"), italic("Dictyuchus"), italic("Eurychasma"), + italic("Geolegnia"), italic("Globisporangium"), italic("Halocrusticida"), + italic("Haptoglossa"), italic("Hyaloperonospora"), italic("Lagena"), + italic("Lagenidium"), italic("Leptolegnia"), italic("Myzocytiopsis"), + italic("Paralagenidium"), italic("Peronospora"), italic("Phragmosporangium"), + italic("Phytophthora"), italic("Phytopythium"), italic("Pilasporangium"), + italic("Plectospira"), italic("Pustula"), italic("Pythiogeton"), + italic("Pythiopsis"), italic("Pythium"), italic("Salilagenidium"), + italic("Saprolegnia"), italic("Thraustotheca"), italic("Wilsoniana")), NA) +}else{ + labelling <- unique(ASVs_funguild$Genus) +} + +guild_g <- ggplot(ASVs_funguild, aes(x = guild_genus, fill = Genus)) + + geom_bar() + shared_theme + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + + ylab("Number of ASVs") + ylim(0,1500) + +guild_s <- ggplot(ASVs_funguild, aes(x = guild_species, fill = Genus)) + + geom_bar() + shared_theme + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + + ylab("Number of ASVs") + ylim(0,1500) + +guild_g_abu <- ggplot(ASVs_funguild, aes(x = guild_genus, + y = Abundance_sum/1000000, + fill = Genus)) + + geom_col() + shared_theme_abu + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + + ylab("Abundance in M reads") + ylim(0,2.5) + +guild_s_abu <- ggplot(ASVs_funguild, aes(x = guild_species, + y = Abundance_sum/1000000, + fill = Genus)) + + geom_col() + shared_theme_abu + + scale_fill_manual(values = colors_oomyc_genera, + na.value = "grey", labels = labelling) + + ylab("Abundance in M reads") + ylim(0,2.5) + +guilds <- ggarrange(guild_g, guild_g_abu, guild_s, + guild_s_abu, nrow = 2, ncol = 2, + common.legend = TRUE, legend = "right" ,align = "hv", + labels = c("Genus level", "", "Species level", ""), + font.label = list(size = 14)) +guilds</code></pre> +<p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>save_plot(guilds ,plot_name = "FunGuild_combo_Fig2") + +cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="rank-samples-by-asv-count-of-classes" class="section level4"> <h4>Rank samples by ASV count of classes</h4> -<p>This chunk generates all panels of Fig. 3, in which the relative abundances of taxonomic classes of interest are summarized. Relative abundances are summed up for all samples in the provided phyloseq objects and assigned a rank based on this sum. The sample with the highest amount of total ASV counts will receive rank 1, the one with the second highest rank 2 and so on. A bar plot is generated, plotting relative abundance against the sample rank with the bars being colored according to the taxonomic. This is supposed to provide a quick overview of relative abundance for the taxonomic class subsets. <img 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" width="672" /></p> +<p>This chunk generates all panels of Fig. 3, in which the relative abundances of taxonomic classes of interest are summarized. Relative abundances are summed up for all samples in the provided phyloseq objects and assigned a rank based on this sum. The sample with the highest amount of total ASV counts will receive rank 1, the one with the second highest rank 2 and so on. A bar plot is generated, plotting relative abundance against the sample rank with the bars being colored according to the taxonomic. This is supposed to provide a quick overview of relative abundance for the taxonomic class subsets.</p> +<pre class="r"><code>## CHANGE ME to change the width (in cm) of the output. +wp <- 17.5 +# CHANGE ME to change the height (in cm) of the output. +hp <- 15 +# CHANGE ME to change the resolution (in dpi) of the output. +res <- 300 + +# calculate averages for display in graphs +avgNames <- c("PPUntr", "PPBait","ChrUntr", "ChrBait", + "O-PUntr", "O-PBait", "OthUntr", "OthBait") +avgs <- c(mean(ranksUntrPP$Abundance), + mean(ranksBaitPP$Abundance), + mean(ranksUntrAlgae$Abundance), + mean(ranksBaitAlgae$Abundance), + mean(ranksUntrOwoP$Abundance), + mean(ranksBaitOwoP$Abundance), + mean(rankedOthersUntr$OtherASVcounts), + mean(rankedOthersBait$OtherASVcounts)) + +names(avgs) <- avgNames + +# multiply to transform relative abundances into avg percentages and round to +# one decimal +avgs <- avgs*100 +avgs <- format(round(avgs, 1), nsmall = 1) + +# Define fixed color schemes for consistency between plots +plotvars <- c("Chrysophyceae", "All Oomycetes", + "Other Oomycetes", "Other classes", "PP") + +# Just to catch others/exceptions, make a general palette that will be +# overwritten below +plotPalette <- viridis(length(plotvars)) +names(plotPalette) <- plotvars +plotPalette["Other classes"] <- "#C0C0C0" +plotPalette["Chrysophyceae"] <- viridis_yellows[2] +plotPalette["PP"] <- viridis_reds[4] +plotPalette["Other Oomycetes"] <- viridis_reds[1] +plotPalette["All Oomycetes"] <- viridis_reds[1] + +# plot total ASV counts, sorted by rank and color by class. +AllBait <- ggplot(data = OthersRankPP, + aes(x = Rank, y = Abundance, fill = Set)) +AllUntr <- ggplot(data = UntrOthersRankPP, + aes(x = Rank, y = Abundance, fill = Set)) + +customTheme <- theme(legend.position = "none", + axis.title = element_blank(), + title = element_text(size = 6)) +customThemeA <- theme(legend.position = "none", + legend.title = element_blank(), + legend.key.size = unit(5,"mm"), + axis.title.x = element_blank()) + +xUpperLim <- nrow(OthersRankPP) + 1 +ytextl = 0.05 +xtextl = 15 +ytexts = 0.75 +xtexts = 45 +middleline <- geom_blank() #geom_hline(yintercept = 0.5, + # linetype="dashed", color="black") + +pAllBait <- AllBait + geom_col() + + geom_col(data = AlgaeInvRankOom) + + geom_col(data = OomRankPP) + + geom_col(data = ranksBaitPP) + + scale_fill_manual(values = plotPalette) + + ggtitle("After enrichment") + xlab("Sample rank") + + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + + customThemeA + middleline + + annotate(geom = 'text', label = paste("Mean PP", avgs["PPBait"], "%"), + x = xtextl, y = ytextl) + + ylab("rel. Abundance") + +pAllUntr <- AllUntr + geom_col() + + geom_col(data = UntrAlgaeInvRankOom) + + geom_col(data = UntrOomRankPP) + + geom_col(data = ranksUntrPP) + + scale_fill_manual(values = plotPalette) + + ggtitle("Before enrichment") + xlab("Sample rank") + + labs(fill = "") + ylim(0,1) + xlim(0,xUpperLim) + + customThemeA + middleline + + annotate(geom = 'text', label = paste("Mean PP", avgs["PPUntr"], "%"), + x = xtextl, y = ytextl) + + ylab("rel. Abundance") + +AlgBait <- ggplot(data = ranksBaitAlgae, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OomBait <- ggplot(data = ranksBaitOwoP, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other oomycetes - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["O-PBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OthBait <- ggplot(data = rankedOthersBait, + aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other classes - after enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["OthBait"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +AlgUntr <- ggplot(data = ranksUntrAlgae, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OomUntr <- ggplot(data = ranksUntrOwoP, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other oomycetes - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["O-PUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +OthUntr <- ggplot(data = rankedOthersUntr, + aes(x = Rank, y = OtherASVcounts, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Other classes - before enrichment") + + xlim(0,xUpperLim) + customTheme + middleline + + annotate(geom = 'text', label = paste("Mean", avgs["OthUntr"], "%"), + x = xtexts, y = ytexts) + + scale_y_continuous(breaks = c(0,0.5,1), limits = c(0,1)) + +#A.combo <- ggarrange(pAllUntr, pAllBait, nrow=2, +# labels = c("A"), vjust = 1, common.legend = TRUE, legend = "bottom") + +untr_smallplots <- ggarrange(AlgUntr, OomUntr, OthUntr, nrow = 3) +untr_smallplots_anot <- annotate_figure(untr_smallplots, + left = text_grob("rel. Abundance", + rot = 90, size = 11)) +bait_smallplots <- ggarrange(AlgBait, OomBait, OthBait, nrow = 3) +bait_smallplots_anot <- annotate_figure(bait_smallplots, + left = text_grob("rel. Abundance", + rot = 90, size = 11)) + +combo <- ggarrange(pAllUntr, untr_smallplots_anot, pAllBait, + bait_smallplots_anot, nrow = 2, ncol = 2, widths = c(2,1), + labels = c("A", "", "B", ""), common.legend = TRUE, + legend = "bottom") + theme(plot.margin = margin(10,10,10,10)) + +#Save plot +save_plot(combo, plot_name = "Class_ranking_Fig3") + +combo</code></pre> +<p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="pairwise-t-tests-of-classes-in-fig.-3" class="section level4"> <h4>Pairwise t-tests of classes in Fig. 3</h4> <p>This chunk documents the pairwise t-tests performed for the classes shown in Fig. 3. For each class/group of classes, the mean relative abundance before and after baiting are compared.</p> +<pre class="r"><code>library(rstatix) +pairwise_test <- function(ASV_list){ + ps.temp <- prune_taxa(ASV_list, ps.trans) + rankTemp <- cuphyr::make_ranked_sums(ps.temp) + rankTemp <- as.data.frame(rankTemp) + teststats <- pairwise_t_test(rankTemp, formula = Abundance ~ Bait) + return(teststats)} + +ChrysUntrVsBait <- pairwise_test(subASVsAlg) +subASVsO_P <- setdiff(subASVsOom, subASVsPP) +OomUntrVsBait <- pairwise_test(subASVsO_P) +PPUntrVsBait <- pairwise_test(subASVsPP) +subASVsOth <- setdiff(taxa_names(ps), subASVsOom) +subASVsOth <- setdiff(subASVsOth, subASVsAlg) +OthUntrVsBait <- pairwise_test(subASVsOth) + +all_stats <- list(PPUntrVsBait, ChrysUntrVsBait, OomUntrVsBait, OthUntrVsBait) +names(all_stats) <- c("PP", "Chrysophyceae", "Other Oomycetes", "Other classes") +all_stats <- bind_rows(all_stats,.id = "Genera from") +all_stats</code></pre> <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> {"columns":[{"label":["Genera from"],"name":[1],"type":["chr"],"align":["left"]},{"label":[".y."],"name":[2],"type":["chr"],"align":["left"]},{"label":["group1"],"name":[3],"type":["chr"],"align":["left"]},{"label":["group2"],"name":[4],"type":["chr"],"align":["left"]},{"label":["n1"],"name":[5],"type":["int"],"align":["right"]},{"label":["n2"],"name":[6],"type":["int"],"align":["right"]},{"label":["p"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["p.signif"],"name":[8],"type":["chr"],"align":["left"]},{"label":["p.adj"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["p.adj.signif"],"name":[10],"type":["chr"],"align":["left"]}],"data":[{"1":"PP","2":"Abundance","3":"Before enrichment","4":"Enriched","5":"60","6":"62","7":"0.03050","8":"*","9":"0.03050","10":"*"},{"1":"Chrysophyceae","2":"Abundance","3":"Before enrichment","4":"Enriched","5":"60","6":"62","7":"0.00818","8":"**","9":"0.00818","10":"**"},{"1":"Other Oomycetes","2":"Abundance","3":"Before enrichment","4":"Enriched","5":"60","6":"62","7":"0.79300","8":"ns","9":"0.79300","10":"ns"},{"1":"Other classes","2":"Abundance","3":"Before enrichment","4":"Enriched","5":"60","6":"62","7":"0.98300","8":"ns","9":"0.98300","10":"ns"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} </script> </div> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> </div> @@ -1929,34 +2966,49 @@ cat("Chunk successfully run")</code></pre> <div id="phylogenetic-tree-of-isolates-asvs-and-reference-isolates" class="section level4"> <h4>Phylogenetic tree of isolates, ASVs and reference isolates</h4> <p>This chunk generates a phylogeny for the isolates ITS sequences and corresponding ASVs and one reference sequence per identified species. The selection/similarity of ASVs was determined by vsearch, the fasta files were assembled with bash commands (Supplementary Fig. 4).</p> -<pre><code>#Trimming isolate sequences to the metabarcoding regions from Sanger consensus multifasta -cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 -o trim_isos_v3.fasta Okay_Consensus_seqs_v3.fa - -#Finding homologous ASVs using vsearch on a one-line-per-sequence (as opposed to multiline fasta) version of the ASV fasta with a similarity cutoff of 98% -vsearch --usearch_global trim_isos_v3.fasta -db ASVs_without_controls_oneline.fasta --userout match_trim-isolates_ASVs_v3.txt -userfields query+target+id+mism+opens -id 0.98 -strand both - -#Parsing results into a list of unique ASVs found +<pre><code># Trimming isolate sequences to the metabarcoding regions from Sanger +# consensus multifasta +cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 \ + -o trim_isos_v3.fasta Okay_Consensus_seqs_v3.fa + +# Finding homologous ASVs using vsearch on a one-line-per-sequence +# (as opposed to multiline fasta) version of the ASV fasta with a similarity +# cutoff of 98% +vsearch --usearch_global trim_isos_v3.fasta -db ASVs_without_controls_oneline.fasta \ +--userout match_trim-isolates_ASVs_v3.txt -userfields query+target+id+mism+opens \ +-id 0.98 -strand both + +# Parsing results into a list of unique ASVs found sed 's/.*ASV/ASV/' match_trim-isolates_ASVs_v3.txt > exact_ASVs_v3.tmp sed -i 's/\t.*//' exact_ASVs_v3.tmp sort exact_ASVs_v3.tmp | uniq > exact_ASVs_v3.txt -#Removing temporary file +# Removing temporary file yes | rm exact_ASVs_v3.tmp -#Extracting fasta belonging to the unique ASVs from the oneline fasta file -grep -f exact_ASVs_v3.txt -A1 ASVs_without_controls_oneline.fasta > exact_ASVs_v3.fasta +# Extracting fasta belonging to the unique ASVs from the oneline fasta file +grep -f exact_ASVs_v3.txt -A1 ASVs_without_controls_oneline.fasta \ +> exact_ASVs_v3.fasta sed -i '/--/d' exact_ASVs_v3.fasta -#Concatenating ASVs and trimmed isolate sequences into a multi-sequence fasta +# Concatenating ASVs and trimmed isolate sequences into a multi-sequence fasta cat exact_ASVs_v3.fasta trim_isos_v3.fasta > iso_and_ASVs_v3.fasta -#Modifying fasta headers for better display in the phylogenetic tree +# Modifying fasta headers for better display in the phylogenetic tree sed -i 's/||.*//' iso_and_ASVs_v3.fasta sed -i 's/@.*//' iso_and_ASVs_v3.fasta -#Trim reference BLAST results from with Genbank identifiers to the region of interest and concatenate with the isolate and ASV sequences -cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 -o BLAST_ref_trim.fa BLAST_ref.fasta +# Trim reference BLAST results from with Genbank identifiers to the region of +# interest and concatenate with the isolate and ASV sequences +cutadapt -a CGGAAGGATCATTACCAC...CAGCAGTGGATGTCTAGGCT --minimum-length 50 \ +-o BLAST_ref_trim.fa BLAST_ref.fasta cat iso_and_ASVs_v3.fasta BLAST_ref_trim.fa > iso_ASVs_refs_v3.fasta</code></pre> +<pre class="r"><code>wp <- 20 +hp <- 15 + +seqs <- readDNAStringSet(file = file.path(path,"iso_ASVs_refs_v3.fasta"), + format = "fasta") +align <- AlignSeqs(DNAStringSet(seqs), iterations = 100, refinements = 5)</code></pre> <pre><code>## Determining distance matrix based on shared 8-mers: ## ================================================================================ ## @@ -1965,12 +3017,12 @@ cat iso_and_ASVs_v3.fasta BLAST_ref_trim.fa > iso_ASVs_refs_v3.fasta</code></ ## Clustering into groups by similarity: ## ================================================================================ ## -## Time difference of 0.03 secs +## Time difference of 0.01 secs ## ## Aligning Sequences: ## ================================================================================ ## -## Time difference of 0.54 secs +## Time difference of 0.51 secs ## ## Iteration 1 of 100: ## @@ -1982,12 +3034,12 @@ cat iso_and_ASVs_v3.fasta BLAST_ref_trim.fa > iso_ASVs_refs_v3.fasta</code></ ## Reclustering into groups by similarity: ## ================================================================================ ## -## Time difference of 0.02 secs +## Time difference of 0.01 secs ## ## Realigning Sequences: ## ================================================================================ ## -## Time difference of 0.4 secs +## Time difference of 0.35 secs ## ## Iteration 2 of 100: ## @@ -1999,34 +3051,197 @@ cat iso_and_ASVs_v3.fasta BLAST_ref_trim.fa > iso_ASVs_refs_v3.fasta</code></ ## Reclustering into groups by similarity: ## ================================================================================ ## -## Time difference of 0.01 secs +## Time difference of 0.02 secs ## ## Realigning Sequences: ## ================================================================================ ## -## Time difference of 0.04 secs +## Time difference of 0.03 secs ## ## Alignment converged - skipping remaining iterations. ## ## Refining the alignment: ## ================================================================================ ## -## Time difference of 0.18 secs +## Time difference of 0.21 secs ## ## Alignment converged - skipping remaining refinements.</code></pre> +<pre class="r"><code>seqs_phang <- phangorn::phyDat(as(align, "matrix"), type = "DNA") +seqs_dm <- phangorn::dist.ml(seqs_phang) +seqs_treeNJ <- NJ(seqs_dm) +seqs_fit = pml(seqs_treeNJ, data = seqs_phang) +fitGTRseqs <- update(seqs_fit, k = 4, inv = 0.2) +fitGTRseqs <- optim.pml(fitGTRseqs, model = "GTR", optInv = TRUE, + optGamma = TRUE, rearrangement = "stochastic", + control = pml.control(trace = 0)) + +GTR_tree <- treeio::as.treedata(fitGTRseqs$tree, type = "ml") +iso_ASV_tree <- ggtree(GTR_tree) + geom_tree() + + geom_treescale(x = 0.5) + + geom_tiplab() + xlim(-0.1,3) + +save_plot(iso_ASV_tree, plot_name = "IsolateSeqs_ASVs_ref_tree_Supfig4") +iso_ASV_tree</code></pre> <p><img 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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="isolates-and-corresponding-asvs" class="section level4"> <h4>Isolates and corresponding ASVs</h4> <p>This chunk generates a tile plot of the presence and abundance of ASVs that correspond with isolates in the soil samples before and after enrichment (Fig. 4).</p> +<pre class="r"><code>##CHANGE ME to change the width (in cm) of the output. +wp <- 12 +#CHANGE ME to change the height (in cm) of the output. +hp <- 10 + +# ASVs (before LULU!) that exactly match isolates seqs (identified by vsearch), +# read in from fasta +exASVs_seqs <- readDNAStringSet(paste0(path,"/exact_ASVs_v3.fasta")) +iso_seqs <- readDNAStringSet(paste0(path,"/trim_isos_v3.fasta")) +iso_ASV_match <- read_delim(paste0(path,"/match_trim-isolates_ASVs_v3.txt"), + delim = "\t", + col_names = c("Iso_Soil" ,"OTU", + "Match", "Mism", "Gap")) + +#Parse headers to get ASV IDs listed in vector +exASVs <- names(exASVs_seqs) %>% str_remove("\\|\\|.*") +isos <- names(iso_seqs) + +#Re-label the facets +enrich_labs <- c("Before enrichment", "After enrichment") +names(enrich_labs) <- c("Before enrichment", "Enriched") + +samp_sums <- ps_tbl %>% + dplyr::group_by(SampleIDs) %>% + summarise(sum_Abundance = sum(Abundance)) + +ps_tr_tbl <- left_join(ps_tbl, samp_sums, by = "SampleIDs") %>% + dplyr::mutate(Rel_Abundance = Abundance/sum_Abundance) %>% + dplyr::mutate(Prcnt_Abundance = Rel_Abundance*100) + +exASV_counts <- ps_tr_tbl %>% + dplyr::select(OTU:Bait, Sanger, Rel_Abundance, Prcnt_Abundance) %>% + dplyr::filter(OTU %in% exASVs) %>% + dplyr::filter(Abundance >= 1) + +iso_ASVs <- ggplot(exASV_counts, aes(x = OTU, + y = reorder(Soil, dplyr::desc(Soil)), + fill = Prcnt_Abundance)) + + geom_tile() + + scale_fill_viridis() + + facet_wrap(~Bait, labeller = labeller(Bait = enrich_labs)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), + panel.grid = element_blank(), + axis.title = element_blank(), + strip.background = element_rect(fill = "white", color = NA)) + + labs(fill = "Abund. (%)") + + scale_x_discrete(limits = exASVs) + +save_plot(iso_ASVs, plot_name = "IsolateSeqs_ASVs_heatmap_Fig4") +iso_ASVs</code></pre> <p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> </div> <div id="supplementary-fig3" class="section level4"> <h4>Supplementary Fig3</h4> -<p>This figure is complementary to Fig 3 in the main manuscript but shows the absolute abundance for PP and Chrysophyceae instead of the relative abundances. <img src="data:image/png;base64,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" width="672" /></p> +<p>This figure is complementary to Fig 3 in the main manuscript but shows the absolute abundance for PP and Chrysophyceae instead of the relative abundances.</p> +<pre class="r"><code>##CHANGE ME to change the width (in cm) of the output. +wp <- 17.5 +#CHANGE ME to change the height (in cm) of the output. +hp <- 10 +#CHANGE ME to change the resolution (in dpi) of the output. +res <- 300 + +#calculate averages for display in graphs +avgNames <- c("PPUntr", "PPBait","ChrUntr", "ChrBait") +avgs <- c(mean(ranksUntrPP_tot$Abundance),mean(ranksBaitPP_tot$Abundance), + mean(ranksUntrAlgae_tot$Abundance),mean(ranksBaitAlgae_tot$Abundance)) +names(avgs) <- avgNames +avgs <- format(round(avgs, 1), nsmall = 1) + +#Define fixed color schemes for consistency between plots +plotvars <- c("Chrysophyceae_tot", "All Oomycetes", + "Other Oomycetes", "Other classes", "PP_tot") +getPalette <- colorRampPalette(viridis(length(plotvars))) +plotPalette <- getPalette(length(plotvars)) +names(plotPalette) <- plotvars +plotPalette["Chrysophyceae_tot"] <- viridis_yellows[2] +plotPalette["PP_tot"] <- viridis_reds[4] + +customTheme <- theme(legend.position = "none", + axis.title = element_blank(), + title = element_text(size = 7)) +customThemeA <- theme(legend.position = "none", + legend.title = element_blank(), + legend.key.size = unit(5,"mm"), + axis.title = element_blank(), + title = element_text(size = 10)) + +xUpperLim <- nrow(ranksBaitAlgae_tot) + 1 +ytextl = 0.05 +xtextl = 15 +ytexts = 90000 +xtexts = 45 + +pAllBait <- ggplot(data = ranksBaitPP_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("PP - after enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["PPBait"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +pAllUntr <- ggplot(data = ranksUntrPP_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("PP - before enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["PPUntr"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +AlgBait <- ggplot(data = ranksBaitAlgae_tot, + aes(x = Rank, y = Abundance, fill = Set)) + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - after enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["ChrBait"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +AlgUntr <- ggplot(data = ranksUntrAlgae_tot, + aes(x = Rank, y = Abundance, fill = Set)) + + geom_col() + + scale_fill_manual(values = plotPalette) + + ggtitle("Chrysophyceae - before enrichment") + + xlim(0,xUpperLim) + + customTheme + + annotate(geom = 'text', label = paste("Mean", avgs["ChrUntr"]), + x = xtexts, y = ytexts) + + scale_y_continuous(limits = c(0,105000)) + +A.combo <- ggarrange(pAllUntr, pAllBait, nrow = 2, labels = c("A"), vjust = 1) +B.combo <- ggarrange(AlgUntr, AlgBait, nrow = 2, labels = c("B"), vjust = 1) + +A.combo.anot <- annotate_figure(A.combo, left = text_grob("Abundance", rot = 90)) + +combo.AB <- ggarrange(A.combo.anot, B.combo, ncol = 2) + + theme(plot.margin = margin(10,10,10,10)) + + scale_fill_manual(values = plotPalette) + + +#Save plot +save_plot(combo.AB, plot_name = "Class_absolute_ranking_Supfig3") +combo.AB</code></pre> +<p><img src="data:image/png;base64,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" width="672" /></p> +<pre class="r"><code>cat("Chunk successfully run")</code></pre> <pre><code>## Chunk successfully run</code></pre> +<pre class="r"><code>sessionInfo()</code></pre> <pre><code>## R version 4.0.2 (2020-06-22) ## Platform: x86_64-w64-mingw32/x64 (64-bit) ## Running under: Windows 10 x64 (build 17763) diff --git a/SupMat3_submission_JoAE.pdf b/SupMat3_submission_JoAE.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7b71059a17e478fbbce3de3b288770a1b176e709 Binary files /dev/null and b/SupMat3_submission_JoAE.pdf differ