This is an R Markdown file containing code to parse the results of a dada2 analysis into phyloseq for further analysis. It is separated into chunks that may be run independently by pressing the play button. You will need 3 files in the same location in order to run this pipeline successfully:
Recommended use: Set the individual chunks until you are content with the ouput, then knit the whole document into a PDF/html, so you have a full record of a successful run.
A custom taxonomy file may be provided instead of using the taxonomy output from dada2. This may be used to supply taxonomy derived e.g. from BLAST searches of the ASVs. Custom taxonomy files must be tab-delimited text with as many rows as the original, colum headers (for all columns except for the first column). For example:
Kingdom Phylum Class Order Family Genus Species
ESV1 Kingdomx Phylumx Classx Orderx Familyx Genusx Speciesx
ESV2 Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy
ESV3 Kingdomz Phylumz Classz Orderz Familyz Genusz Speciesz
… ESVn Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy
Friendly warning: Parsing the results of a BLAST search into this format may require some effort.
‘descriptors.txt’ should be a tab-delimited .txt table describing the samples. It must have the same length and order as the samples in seqtab_nochim.rds. To check the order and length of samples in seqtab_nochim.rds and generate a template to fill out, you may run the chunk below with “optional_sample_check” set to “TRUE”.
Any number of descriptors is possible. The sample names may be retained as one descriptor, but this is not necessary, as they will be added during parsing. For example, if there are 4 samples (order: s1, s2, s3, s4), the txt file could look as follows:
Subject Species Time
Kar1 A.thaliana 24hpi
Kar1 A.thaliana 72hpi
Mec2 S.tuberosum 24hpi
Mec3 S.tuberosum 24hpi
Finally, the file should end with an empty line, since it may throw an error otherwise. However, this is usually not a serious problem.
If you choose to use the blank file, you MUST retain the original order of the samples!
This chunk also loads required packages and defines the location of the input files. It requires the correct path as input, and allows setting the pruning of control samples and choosing generation of a phylogenetic tree. Beware: The generation of a phylogenetic tree may take several days for >1000 sequences, it is therefore recommended to only use this feature for the final analysis or small sample sets. This scricpt assumes the packages Biostrings, dada2, DECIPHER, ggplot2, ggsci, phangorn, phyloseq and stringr to be installed.
# CHANGE ME to the directory that contains 'seqtab_nochim.rds'
path = "FITS1_DADA2_results_260821/"
# CHANGE ME to TRUE to list all samples and generate an empty metadata file
optional_sample_check = TRUE
# CHANGE ME to TRUE to update cuphyr
update_cuphyr = TRUE
# Initiate by loading packages and setting knit options
################# NO CHANGES NECESSARY BELOW #################
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(root.dir = paste0(path))
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)
if (update_cuphyr) {
devtools::install_github("simeross/cuphyr")
}
# Sequence and microbiome specific libraries
library(dada2)
library(Biostrings)
library(DECIPHER)
library(cuphyr)
# The export of phyloseq objects to a BIOM format and the generation of fancier
# ordination plots require the phyloseq-extended package. The first command
# installs the package that is currently on the dev brach of the author's
# repository, the second command sources some extra functions, including the
# better ordination plot implementation.
remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
library(phyloseq)
#library(SIAMCAT)
# Phylogeny libraries
library(phangorn)
library(ape)
# Plotting and figure export
library(gridExtra)
library(viridis)
library(ggpubr)
# Tidyverse
library(tidyverse)
library(stringr)
# Various packages for specific analysis
library(readxl)
library(openxlsx)
library(ggpmisc)
library(betareg)
library(BBmisc)
library(aod)
library(betareg)
#install.packages('MicrobiomeStat')
library(MicrobiomeStat)
# Checks whether output path exists and creates it if not. Throws warning if
# directory exists.
outp <- paste0(path,"/analysis_output")
dir.create(file.path(outp))
if (optional_sample_check) {
seqtabcheck <- readRDS(paste0(path,"/seqtab_nochim.rds"))
samps <- rownames(seqtabcheck)
lensamps <- length(samps)
blankcol <- vector(mode = "character", length = lensamps)
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")
rm(optional_sample_check, seqtabcheck, samps,
lensamps, blankcol, blanktable, update_cuphyr)
}else{rm(optional_sample_check, update_cuphyr)}
## 'seqtab_nochim.rds' contains samples in the following order:
##
## 1-its1
## 10-its1
## 11-its1
## 12-its1
## 13-its1
## 14-its1
## 15-its1
## 16-its1
## 17-its1
## 18-its1
## 2-its1
## 3-its1
## 4-its1
## 5-its1
## 6-its1
## 7-its1
## 8-its1
## 9-its1
## NegativK-4-its1
## PositivK-1-its1
## PositivK-2-its1
## PositivK-3-its1
## PositivK-4-its1
## Vann1-its1
## Vann2-its1
##
## The number of samples in the file is:
## 25
This chunk allows the adjustment of several 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.
# Dedicated environment containing all global analysis settings for better
# overview and collected export of settings
parameters <- new.env()
# CHANGE ME to 'TRUE' to remove control samples from the analysis or 'FALSE' to
# analyse all samples.
parameters$prune_controls = "TRUE"
# CHANGE ME to a list of unique identifiers that only occur in the names of
# samples you do NOT want to analyse. Common examples are provided.
parameters$controls = c("Pos", "H2O", "Neg", "Kontr", "Contr", "POSK")
# CHANGE ME to 'TRUE' to remove certain taxonomic groups from the analysis by
# name. This is useful to exclude non-target organisms or noise from organelles
# such as Chloroplasts and Mitochondria. It is recommended to first look at all
# data before using this setting.
parameters$prune_noise_taxgroups = "FALSE"
# CHANGE ME to define the taxonomic groups to be removed as noise.
parameters$noise_taxgroups = c("Chloroplast", "Mitochondria")
# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
# minimally have. Samples with lower ASV counts than 'minread' will be pruned.
# Set to 0 to not prune any samples.
parameters$minASVcount = 3000
# CHANGE ME to 'TRUE', if you want to provide a custom taxonomy table instead
# of using the default dada2 output ('taxa.rds').
parameters$customTax = "TRUE"
# CHANGE ME to the location of the custom taxonomy file. This only matters if
# parameters$customTax='TRUE', otherwise it will be ignored.
parameters$taxfile = "FITS1_DADA2_results_260821/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 'parameters$maketree'
parameters$maketree = "FALSE"
# 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
parameters$roottree = "TRUE"
## CHANGE ME to 'TRUE' to export all generated phyloseq objects as .biom
## objects
parameters$biom_export = "FALSE"
This chunk loads the input data into a usable format.This chunk does not require any user inputs. If no phylogenetic tree with the name ‘phylotree.rds’ was provided and ‘parameters$maketree=“TRUE”’, it will be calculated here. The phylogenetic tree is necessary for certain plots that incorporate ‘true’ taxonomic relationships beyond the annotations, such as PCoA.
############### NO NEED FOR CHANGES BELOW ############### Make dedicated
############### environments to contain temporary values and manage other
############### objects
tmp <- new.env()
plots <- new.env()
set <- new.env()
# Read in variables
tmp$seqtabp <- readRDS(paste0(path, "/seqtab_nochim.rds"))
if (parameters$customTax == "TRUE") {
tmp$taxap <- read.delim(parameters$taxfile, header = TRUE, sep = "\t")
rownames(tmp$taxap) <- colnames(tmp$seqtabp)
tmp$taxap <- as.matrix(tmp$taxap)
} else {
tmp$taxap <- readRDS(paste0(path, "/taxa.rds"))
}
tmp$samp_table <- read.delim(paste0(path, "/descriptors.txt"), header = TRUE, sep = "\t")
tmp$samp_list <- rownames(tmp$seqtabp)
# Check if descriptors has the same samples as seqtabp
if (length(tmp$samp_table[, 1]) != length(tmp$samp_list)) {
stop("There are ", length(tmp$samp_table[, 1]), " samples in 'descriptors.txt', but ",
length(tmp$samp_list), " samples in 'seqtab_nochim.rds'. Please make sure that the correct samples
are contained in descriptors.txt.
You may use 'optional_sample_check <- TRUE' in the first chunk to generate an
empty template for 'descriptors.txt'")
} else if (!identical(tmp$samp_table[, 1], tmp$samp_list)) {
warning("Warning: The samples in 'descriptors.txt' do not have the same names
or order as the samples in 'seqtab_nochim.rds'. This may be fine if
abbreviated names were used or the sample names are not contained in
the first column of 'descriptors.txt'. Double-checking never hurts!")
}
# generate phylogenetic tree of ASVs only if there is no file called
# 'phylotree.rds' in the working directory and 'parameters$maketree' is 'TRUE'
if (!file.exists(paste0(path, "/phylotree.rds"))) {
if (parameters$maketree == "TRUE") {
tmp$ASVs <- getSequences(tmp$seqtabp)
names(tmp$ASVs) <- tmp$ASVs
tmp$ASV_align <- AlignSeqs(DNAStringSet(tmp$ASVs), anchor = NA)
tmp$ASV_phang <- phyDat(as(tmp$ASV_align, "matrix"), type = "DNA")
tmp$dm <- dist.ml(tmp$ASV_phang)
tmp$treeNJ <- NJ(tmp$dm)
tmp$fit <- pml(tmp$treeNJ, data = tmp$ASV_phang)
tmp$fitGTR <- update(tmp$fit, k = 4, inv = 0.2)
tmp$fitGTR <- optim.pml(tmp$fitGTR, model = "GTR", optInv = TRUE, optGamma = TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))
saveRDS(tmp$fitGTR, file = paste0(path, "/phylotree.rds"))
}
}
## parse into phyloseq object
row.names(tmp$samp_table) <- tmp$samp_list
if (file.exists(paste0(path, "/phylotree.rds"))) {
tmp$treep <- readRDS(paste0(path, "/phylotree.rds"))
p <- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
tax_table(tmp$taxap), phy_tree(tmp$treep$tree))
} else {
p <- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
tax_table(tmp$taxap))
}
## Adding nucleotide info and giving sequences ASV## identifiers
tmp$ASV_sequences <- Biostrings::DNAStringSet(taxa_names(p))
taxa_names(p) <- paste0("ASV", seq(ntaxa(p)))
names(tmp$ASV_sequences) <- taxa_names(p)
p <- merge_phyloseq(p, tmp$ASV_sequences)
## optional pruning
if (parameters$prune_controls == "TRUE") {
if (!is.null(parameters$controls)) {
tmp$samp_clean <- tmp$samp_list[!tmp$samp_list %in% grep(paste0(parameters$controls,
collapse = "|"), tmp$samp_list, value = T)]
tmp$contr_pruned <- setdiff(tmp$samp_list, tmp$samp_clean)
ps <- prune_samples(tmp$samp_clean, p)
# Physeq object for Just controls
ps.contr <- prune_samples(tmp$contr_pruned, p)
ps.contr <- prune_taxa(taxa_sums(ps.contr) > 0, ps.contr)
ps.transcontr <- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV))
message(cat("\n", "Number of control samples that were pruned and will not be analysed:\n",
length(tmp$samp_list) - length(tmp$samp_clean), "\n", "The following controls were pruned:\n",
tmp$contr_pruned, "The controls are contained in a separate phyloseq object: ps.contr",
"\n", sep = "\n"))
} else {
warning(cat("\n\nparameters$prune_controls is TRUE but 'parameters$controls' is empty.
No samples were pruned.\n\n"))
}
} else {
ps <- p
}
##
##
## Number of control samples that were pruned and will not be analysed:
##
## 5
##
##
## The following controls were pruned:
##
## NegativK-4-its1
## PositivK-1-its1
## PositivK-2-its1
## PositivK-3-its1
## PositivK-4-its1
## The controls are contained in a separate phyloseq object: ps.contr
# Prune ASVs defined as noise
if (parameters$prune_noise_taxgroups == "TRUE") {
tmp$ps_taxlvls <- colnames(tax_table(ps))
tmp$noise_ASVs <- character(0)
for (lvl in tmp$ps_taxlvls) {
tmp$noise_ASVs <- c(tmp$noise_ASVs, cuphyr::list_subset_ASVs(physeq = ps,
subv = parameters$noise_taxgroups, taxlvlsub = lvl))
}
tmp$noise_ASVs <- unique(tmp$noise_ASVs)
tmp$no_noise_ASVs <- colnames(otu_table(ps))
tmp$no_noise_ASVs <- setdiff(tmp$no_noise_ASVs, tmp$noise_ASVs)
if (length(tmp$noise_ASVs) > 0) {
ps <- prune_taxa(tmp$no_noise_ASVs, ps)
tmp$no_noise_ps <- ps
cat(length(tmp$noise_ASVs), "ASVs were pruned because they belonged to the following
taxonomic groups:\n")
cat(parameters$noise_taxgroups, "\n", sep = "\n")
} else {
cat("No ASVs were recognized as belonging to the following taxonomic groups
defined as noise:\n")
cat(parameters$noise_taxgroups, "\n", sep = "\n")
}
}
# Prune samples with fewer than reads than minASVcount
if (parameters$minASVcount > 0) {
tmp$samp_pruned <- names(which(sample_sums(ps) < parameters$minASVcount))
ps <- prune_samples(sample_sums(ps) >= parameters$minASVcount, ps)
if (length(tmp$samp_pruned) > 0) {
cat("The following samples were pruned because ASV counts were lower than",
parameters$minASVcount, ":\n")
cat(tmp$samp_pruned, "\n", sep = "\n")
}
}
## The following samples were pruned because ASV counts were lower than 3000 :
## Vann1-its1
## Vann2-its1
# Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
ps <- prune_taxa(taxa_sums(ps) > 0, ps)
# Get a tbl of the base object for easier access in some phyloseq-independent
# analyses. Takes some seconds, potentially up to minutes.
ps_tbl <- as_tibble(psmelt(ps))
# Transformed per sample (per-sample relative abundance)
ps.trans <- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
# Read NDVI values as numeric
sample_data(ps)[["ndvi"]] <- as.numeric(sample_data(ps)[["ndvi"]])
sample_data(ps.trans)[["ndvi"]] <- as.numeric(sample_data(ps.trans)[["ndvi"]])
# Get a tbl of the base object for easier access in some phyloseq-independent
# analyses. Takes some seconds, potentially up to minutes.
ps_tbl <- as_tibble(psmelt(ps))
ps_trans_tbl <- as_tibble(psmelt(ps.trans))
# Condensing to Abundance per Genus and Sample
genus_abundance_tbl_per_sample <- ps_trans_tbl %>%
group_by(Genus, Sample) %>%
mutate(Genus_Sample_Abundance = sum(Abundance)) %>%
select(Genus, Sample, ndvi, Genus_Sample_Abundance, Alias) %>%
unique()
if (parameters$roottree == "TRUE" && parameters$maketree == "TRUE") {
phyloseq::phy_tree(ps) <- cuphyr::root_tree_in_outgroup(physeq = ps)
}
if (parameters$biom_export == "TRUE") {
suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
"all_samples.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
"samples_without_controls.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
"samples_without_controls_rel_abundance.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
"just_controls.biom"), biom_format = "standard"))
}
ps
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1013 taxa and 18 samples ]
## sample_data() Sample Data: [ 18 samples by 19 sample variables ]
## tax_table() Taxonomy Table: [ 1013 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 1013 reference sequences ]
The chunks below will produce various plots and other output. Each chunk is headed by a description of the output and may contain some parameters to adjust the output.
This chunk sets the background structure and color palette. Viridis was chosen because it is optimized for grey-scale printing and various types of color blindness and More info on the Viridis palette can be found on the Viridis info page. It also establishes save_plot as a shorter variant of ggsave with customized date-time structure to save plots with the same name mulitple times instead of overwriting them (overwriting can be triggered with overwrite=TRUE).
##### Optional settings (sensible defaults) #####
# Can be changed to adjust the output format for all plots. Default "pdf",
# possible "eps"/"ps", "tex" (pictex), "jpeg", "tiff", "png", "bmp" and "svg"
parameters$output_format = "pdf"
# Can be changed to preferred ggplot2 theme. Recommended: "theme_bw()".
theme_set(theme_bw())
############### NO NEED FOR CHANGES BELOW ###############
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
tmp$sort_colors <- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20)))
# Customized vectors
tmp$n_col <- 20
tmp$viridis_greens <- viridis(tmp$n_col, option = "D", begin = 0.85,
end = 0.7)[tmp$sort_colors]
tmp$viridis_reds <- viridis(tmp$n_col, option = "B", begin = 0.7,
end = 0.5)[tmp$sort_colors]
tmp$viridis_blues <- viridis(tmp$n_col, option = "D", begin = 0.2,
end = 0.4)[tmp$sort_colors]
tmp$viridis_yellows <- viridis(tmp$n_col, option = "D", begin = 1,
end = 0.9)[tmp$sort_colors]
tmp$viridis_dark <- viridis(tmp$n_col, option = "A", begin = 0,
end = 0.1)[tmp$sort_colors]
tmp$viridis_light <- viridis(tmp$n_col, option = "A", begin = 1,
end = 0.9)[tmp$sort_colors]
# Collected list that is available in the global environment
sub_viridis <- list(tmp$viridis_greens, tmp$viridis_blues, tmp$viridis_yellows,
tmp$viridis_light, tmp$viridis_reds, tmp$viridis_dark)
names(sub_viridis) <- c("greens", "blues", "yellows", "lights", "reds", "darks")
tmp$out <- paste0(".", parameters$output_format)
#################### Function ############################
# 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. Overwriting can
# be triggered with overwrite = TRUE. Width, height and resolution are taken
# from parameters in the 'set' environment or set to 20x20 cm with 300dpi.
save_plot <- function(
pl, filetype = ".pdf", plot_name = "my_plot", overwrite=FALSE){
wp <- if (!is.null(set$wp)) set$wp else 20
hp <- if (!is.null(set$hp)) set$hp else 20
res <- if (!is.null(set$res)) set$res else 300
name <- paste0("/", plot_name,filetype)
if (file.exists(paste0(outp, name)) & !overwrite) {
name <- paste0("/", plot_name, "_",
format(Sys.time(), "%d-%m-%y_%H%M%S"),filetype)}
ggsave(file.path(outp, name), pl,
width = wp, height = hp, unit = "cm", dpi = res)
}
################################################
This chunk plots the absolute abundance of all samples (including controls) and all samples without controls and other trimmed samples.
# CHANGE ME to the sample group for color coding. Accepted values are the column
# headers in the descriptor file.
set$color_by = "prat_mol_asv"
##### Optional settings (sensible defaults) #####
# Can be changed to change the width (in cm) of the saved plot.
set$wp = 17
# Can be changed to change the height (in cm) of the saved plot.
set$hp = 20
# Can be changed to change the resolution (in dpi) of the saved plot.
set$res = 300
############### NO NEED FOR CHANGES BELOW ###############
# Rank samples
set$ranked <- cuphyr::make_ranked_sums(p, myset = tmp$subset_id)
set$ranked_ps <- cuphyr::make_ranked_sums(ps, myset = tmp$subset_id)
set$ymax <- max(set$ranked$Abundance)
set$ymax <- set$ymax + round(set$ymax/10)
set$xmax <- nrow(set$ranked) + 1
set$title2 <- "Samples (without controls)"
# Stabilize colors
set$color_vars <- set$ranked[,set$color_by] %>%
unlist() %>% as.character() %>% unique()
set$color_vars <- sort(set$color_vars)
set$color_varsPalette <- viridis(length(set$color_vars))
names(set$color_varsPalette) <- set$color_vars
set$my_scale_fill <- scale_fill_manual(values = set$color_varsPalette)
# plot
plots$overview_all <- ggplot(data = set$ranked, aes(x = Rank, y = Abundance)) +
aes_string(fill = set$color_by) +
geom_col() + set$my_scale_fill + ggtitle("All samples") + ylim(0, set$ymax) +
xlim(0,set$xmax) + ylab("ASV counts ('reads')")
if (length(tmp$noise_ASVs) > 0) {
set$ranked_nonoise <- cuphyr::make_ranked_sums(
tmp$no_noise_ps, myset = tmp$subset_id)
plots$overview_noise <- ggplot(
data = set$ranked_nonoise, aes(x = Rank, y = Abundance)) +
aes_string(fill = set$color_by) +
geom_col() + set$my_scale_fill +
ggtitle("Samples (without controls), noise ASVs removed") +
ylim(0, set$ymax) +
xlim(0,set$xmax) + ylab("ASV counts ('reads')")
}
if (parameters$minASVcount > 0) {
plots$overview_all <- plots$overview_all +
geom_hline(yintercept = parameters$minASVcount, linetype = "dashed") +
ggtitle("All samples (ASV count cutoff indicated)")
set$title2 <- "Samples (without controls and low count samps)"
}
plots$overview_ps <- ggplot(data = set$ranked_ps, aes(x = Rank, y = Abundance)) +
aes_string(fill = set$color_by) +
geom_col() + set$my_scale_fill + ggtitle(set$title2) + ylim(0, set$ymax) +
xlim(0,set$xmax) + ylab("ASV counts ('reads')")
plots$combo_overview <- ggarrange(
plots$overview_all, plots$overview_ps, nrow = 2, align = "v",
common.legend = TRUE, legend = "right")
if (parameters$minASVcount > 0) {
plots$combo_overview <- ggarrange(
plots$overview_all, plots$overview_noise, plots$overview_ps,
nrow = 3, align = "v",
common.legend = TRUE, legend = "right")
}
#Save plots
save_plot(plots$combo_overview, plot_name = "Overview_all_and_pruned",
filetype = tmp$out)
#Clean up plot parameters
rm(list = ls(set), envir = set)
#Print plots
plots$combo_overview
This chunk generates an overview over the controls (positive AND negative)
# CHANGE ME to the desired sample categories on the x-axis. In this case it
# should be the Sample names.
set$x_axis_value = "SampleIDs"
# CHANGE ME to the taxonomic level for color coding. Use "OTU" for ASVs,
# "Genus", "Species" or "OTU" recommended to compare pos. controls.
set$color_by_taxlvl = "Species"
# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic
# tree is available). Use "OTU" for ASVs.
set$label_by_taxlvl = "OTU"
# CHANGE ME to a sample category to shape the tree tip labels by (if
# phylogenetic tree is available).
set$label_shape_by = "Symptom"
##### Optional settings (sensible defaults) #####
# Can be changed to generate a tree for just the control sequences IF no
# phylogenetic tree for all seuquences is provided. This may slow down this
# chunk when running it for the first time
set$control_tree = TRUE
# Can be changed to change the width (in cm) of the saved plot.
set$wp = 17
# Can be changed to change the height (in cm) of the saved plot.
set$hp = 20
# Can be changed to change the resolution (in dpi) of the saved plot.
set$res = 300
############### NO NEED FOR CHANGES BELOW ###############
if (set$control_tree & class(try(phy_tree(ps.transcontr),
silent = TRUE)) == "try-error") {
# generate phylogenetic tree of ASVs only if there is no file called
# 'phylotree.rds' in the working directory and 'parameters$maketree' is "TRUE"
if (!file.exists(paste0(path, "/controls_phylotree.rds"))) {
set$ASVs <- phyloseq::refseq(ps.transcontr)
set$ASV_align <- AlignSeqs(set$ASVs, anchor = NA)
set$ASV_phang <- phyDat(as(set$ASV_align, "matrix"), type = "DNA")
set$dm <- dist.ml(set$ASV_phang)
set$treeNJ <- NJ(set$dm)
set$fit <- pml(set$treeNJ, data = set$ASV_phang)
set$fitGTR <- update(set$fit, k = 4, inv = 0.2)
set$fitGTR <- optim.pml(set$fitGTR, model = "GTR",
optInv = TRUE, optGamma = TRUE,
rearrangement = "stochastic",
control = pml.control(trace = 0))
saveRDS(set$fitGTR, file = paste0(path, "/controls_phylotree.rds"))}
set$fitGTR <- readRDS(paste0(path, "/controls_phylotree.rds"))
phyloseq::phy_tree(ps.transcontr) <- set$fitGTR$tree
}
plots$topnpplot <- plot_bar(ps.contr, x = set$x_axis_value,
fill = set$color_by_taxlvl) + my_scale_fill +
theme(axis.title.x = element_blank(), legend.position = "none",
legend.key.size = unit(3, "mm")) +
ylab("ASV counts") + guides(col = guide_legend(ncol = 3))
plots$topntplot <- plot_bar(ps.transcontr, x = set$x_axis_value,
fill = set$color_by_taxlvl) + my_scale_fill +
theme(axis.title.x = element_blank(), legend.position = "none",
legend.key.size = unit(3, "mm")) +
ylab("Relative abundance") + guides(col = guide_legend(ncol = 3))
plots$combo_contr <- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2,
labels = c("A", "B"), align = "hv",
common.legend = TRUE, legend = "right")
if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != "try-error") {
plots$tre <- plot_tree(
ps.transcontr, ladderize = "left", label.tips = set$label_by_taxlvl,
color = "abundance", text.size = 2.5, shape = set$label_shape_by) +
scale_color_viridis_c(aesthetics = c("color","fill")) +
theme(legend.position = "left", panel.border = element_blank())
plots$combo_contr <- ggarrange(plots$tre, ggarrange(plots$topnpplot,
plots$topntplot, ncol = 2,
labels = c("B", "C"), align = "hv",
common.legend = TRUE, legend = "right"),
nrow = 2, legend = "right", labels = c("A"))
}
# save
save_plot(plots$combo_contr, plot_name = "Controls", filetype = tmp$out)
plots$combo_contr
This chunk lists the top n most abundant taxonomic terms at a given
level. Change the function parameters to the desired values. For more
info, check help page of the function with
?cuphyr::abundant_tax_physeq()
. Change ‘ignore_na’ to
include/exclude NA values at the given level.
#The character vector can later be accessed by calling 'tmp$tops'
tmp$tops <- cuphyr::abundant_tax_physeq(physeq = ps,
lvl = "Genus",
top = 10,
output_format = "tops",
ignore_na = TRUE,
silent = FALSE)
##
## The top 10 most abundant annotated groups at the taxonomic level 'Genus' are:
## Mrakia
## Mortierella
## Sistotrema
## Candida
## Solicoccozyma
## Juxtiphoma
## Saitozyma
## Sporidiobolus
## Tausonia
## Naganishia
This chunk gives an overview of the number of ASVs belonging to which Phylum
fits1_list <- cuphyr::list_subset_ASVs(subv = c("Fungi"), taxlvlsub = "Kingdom")
cuphyr::summarise_physeq(physeq = ps,
ASV_sublist = fits1_list,
sublist_id = "Fungi", samp_names = FALSE)
## There are 1013 ASVs in the phyloseq object 'ps'.
## Of this, 982 belong to the provided subset (Fungi), representing 98.17 percent of abundance per sample on average.
fits1_list <- cuphyr::list_subset_ASVs(subv = c("Fungi"), taxlvlsub = "Kingdom")
cuphyr::summarise_physeq(physeq = ps,
ASV_sublist = fits1_list,
sublist_id = "Fungi", samp_names = FALSE)
## There are 1013 ASVs in the phyloseq object 'ps'.
## Of this, 982 belong to the provided subset (Fungi), representing 98.17 percent of abundance per sample on average.
ps_trans_tbl %>% select(OTU, Phylum) %>% unique() %>% group_by(Phylum) %>% summarise(length(OTU))
## # A tibble: 17 × 2
## Phylum `length(OTU)`
## <chr> <int>
## 1 'uncultured eukaryote' 2
## 2 'uncultured fungus' 223
## 3 Apicomplexa 1
## 4 Arthropoda 13
## 5 Ascomycota 337
## 6 Basidiomycota 244
## 7 Chlorophyta 2
## 8 Ciliophora 1
## 9 Mollusca 2
## 10 Mucoromycota 114
## 11 Nematoda 1
## 12 Olpidiomycota 9
## 13 Platyhelminthes 1
## 14 Rhodophyta 1
## 15 Streptophyta 4
## 16 Zoopagomycota 2
## 17 <NA> 56
# Total ASV counts
ps_tbl %>% select(OTU, Abundance) %>% summarise(total_sum = sum(Abundance))
## # A tibble: 1 × 1
## total_sum
## <int>
## 1 1246983
# Total ASV counts per phylum
ps_tbl %>% select(OTU, Kingdom, Abundance) %>% group_by(Kingdom) %>% summarise(Abundance_sum = sum(Abundance))
## # A tibble: 4 × 2
## Kingdom Abundance_sum
## <chr> <int>
## 1 Fungi 1219571
## 2 Metazoa 27068
## 3 Viridiplantae 211
## 4 <NA> 133
# Percentage of ASV counts belonging to Fungi
its1_percentage = 1219571/1246983 # ASV counts Fungi / Total ASV counts
its1_percentage
## [1] 0.9780173
This chunk plots abundance of the Top n ASVs or 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. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n may lead to unreadable plots. The chunk does not require any input, but it is possible to adjust the default ‘n’, and to change width, height and resolution of the PDF-output if necessary.
# CHANGE ME to the desired sample categories on the x-axis.
# Accepted values are the column headers in the descriptor file.
set$x_axis_value = "ndvi"
# CHANGE ME to the taxonomic level of interest (color coding). Accepted values
# are the column headers in your descriptor file.
set$taxlvl = "Genus"
# CHANGE ME to change the number of Top n taxa to be plotted at
# taxlvl.
set$top_n = 10
# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
set$ignore_na = FALSE
# CHANGE ME to an entry at the chosen taxonomic level you want to highlight.
# Comment out to not highlight anything.
#set$highlight = "Meloidogyne"
##### Optional settings (sensible defaults) #####
# Can be changed to change the width (in cm) of the saved plot.
set$wp = 20
# Can be changed to change the height (in cm) of the saved plot.
set$hp = 13
# Can be changed to change the resolution (in dpi) of the saved plot.
set$res = 300
# Can be changed to change the y-axis label
set$y_axis_label = "Relative abundance"
# Can be changed to change the x-axis label
set$x_axis_label = "Sample"
############### NO NEED FOR CHANGES BELOW ###############
# Estimate Alpha-diversity (Shannon)
set$alpha_div_ps_trans <- estimate_richness(ps.trans, measures = "Shannon") %>%
as_tibble(rownames = "Sample")
#rename("Shannon" = Simpson)
# Make physeq objects of top n taxa and top n ASVs
set$ps.topnTax <- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl,
top = set$top_n,
ignore_na = set$ignore_na)
# Plot
set$my_scale_fill <- my_scale_fill
set$topntax_tbl <- psmelt(set$ps.topnTax) %>%
as_tibble() %>%
left_join(set$alpha_div_ps_trans, by = "Sample") %>%
select(Genus, Alias, ndvi, Abundance, Shannon) %>%
filter(Abundance > 0) %>%
group_by(Genus, Alias, ndvi, Shannon) %>%
summarise(Abundance = sum(Abundance)) %>%
arrange(ndvi) %>%
mutate(ndvi_rank = c(1:length(ndvi)))
plots$topn_tax_custom <- ggplot(set$topntax_tbl, aes(
x = fct_reorder(Alias, ndvi),
y = Abundance,
fill = Genus)) +
#title = paste0("Top ", set$top_n, " ", set$taxlvl))) +
geom_col(color = "black") +
set$my_scale_fill +
ylab(set$y_axis_label) +
xlab(set$x_axis_label) +
theme(strip.background = element_blank(),
legend.position = "bottom")
plots$ndvi_dot_plot <- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
y = ndvi)) +
geom_point() +
theme(strip.background = element_blank(),
axis.title.x=element_blank()) +
ylab("NDVI")
plots$shannon_dot_plot <- ggplot(set$topntax_tbl,
aes(fct_reorder(Alias, ndvi),
y = Shannon)) +
geom_point() +
theme(strip.background = element_blank(),
axis.title.x=element_blank()) +
ylab("Shannon")
plots$combo_topn_custom <- ggarrange(plots$ndvi_dot_plot,
plots$shannon_dot_plot,
plots$topn_tax_custom,
nrow = 3,
heights = c(1, 1, 3),
align = "v")
save_plot(plots$combo_topn_custom, plot_name = paste0("Customized_NDVI_Shannon_plot"),
filetype = tmp$out)
plots$combo_topn_custom
topntax_data <- set$topntax_tbl %>%
mutate(Taxa = 'fits1') %>%
ungroup() %>%
select(Alias, ndvi, Shannon, Taxa) %>%
distinct()
write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_fits1.csv")
# Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
# containing the sample abundances for that genus.
ldf <- data.frame(genus_abundance_tbl_per_sample %>% pivot_wider(id_cols = c('Sample', 'ndvi'), names_from = 'Genus', values_from = 'Genus_Sample_Abundance'))
ldf_genus_data <- data.frame(ldf) %>% select(!c('Sample', 'ndvi'))
colnames(ldf_genus_data) <- gsub(' ', '.', colnames(ldf_genus_data))
ldf <- cbind.data.frame(
Sample = ldf$Sample,
ndvi_01 = (ldf$ndvi + 1) / 2.0
)
ldf <- tibble(cbind(ldf, ldf_genus_data))
n_samples_by_genus <- data.frame(ldf_genus_data > 0) %>% mutate_if(is.logical, as.numeric) %>% colSums() %>% sort(decreasing = TRUE)
keep_n <- 100 # Maximum number of genuses to include in the analysis
top_n_occurence_genuses <- names(n_samples_by_genus[1:keep_n])
top_n_occurence_genuses <- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
l_genus_ldf <- ldf %>% select(all_of(top_n_occurence_genuses))
l_genus_ldf_transposed <- data.frame(t(l_genus_ldf))
l_meta_ldf <- ldf %>% select('ndvi_01')
l_model <- linda(
l_genus_ldf_transposed,
l_meta_ldf,
formula = '~ ndvi_01',
feature.dat.type = 'proportion',
is.winsor = FALSE,
alpha = 0.05
)
## 0 features are filtered!
## The filtered data has 18 samples and 100 features will be tested!
## Fit linear models ...
## Completed.
# Print model info
l_model
## $variables
## [1] "ndvi_01"
##
## $bias
## [1] -0.1560564
##
## $output
## $output$ndvi_01
## baseMean log2FoldChange lfcSE stat
## Mrakia 340184.59007 -0.204087169 0.15117277 -1.350026034
## Candida 44204.60411 0.313554734 0.33157232 0.945660163
## Sistotrema 74105.32511 0.110212031 0.29324305 0.375838517
## Bactrocera 7806.33499 0.679090318 0.43293090 1.568588222
## Mortierella 192461.65172 -0.043290897 0.10349192 -0.418302196
## Sporidiobolus 14874.06202 1.578834180 0.32349836 4.880501396
## Juxtiphoma 39774.07212 0.014101235 0.13977953 0.100881977
## Tausonia 26322.26036 -0.093883636 0.20364465 -0.461016964
## Naganishia 25647.20607 0.369171189 0.18162449 2.032606935
## Saitozyma 34589.98512 0.236064479 0.07448041 3.169484105
## Botrytis 14364.12697 0.695648156 0.31302174 2.222363697
## Solicoccozyma 46636.49367 0.013954771 0.06928624 0.201407523
## Holtermanniella 12365.63902 0.157407640 0.17455343 0.901773426
## Tetracladium 14309.09600 -0.769677521 0.19939424 -3.860079020
## Ascobolus 4672.21703 -0.242018800 0.22181684 -1.091074945
## Trichosporiella 10518.07870 -0.441756988 0.18542568 -2.382393812
## Dendryphion 3285.61699 -0.518529490 0.20130949 -2.575782666
## Papiliotrema 10698.66436 0.140491327 0.15715957 0.893940617
## Incrupila 6995.67328 -0.411028144 0.13784184 -2.981882344
## Paraphoma 1637.31059 -0.561378782 0.46430853 -1.209064120
## Paraphaeosphaeria 6106.05411 -0.155745024 0.16111694 -0.966658302
## Didymella 1307.00291 -0.260531888 0.35851067 -0.726706098
## Haplosporangium 4078.80668 0.150041293 0.15062673 0.996113350
## X.uncultured.fungus. 16818.86991 0.103333263 0.14308774 0.722167139
## Burgoa 1522.54815 0.637244740 0.33030186 1.929279894
## Pseudoanguillospora 1166.01391 -0.132895032 0.31691179 -0.419343919
## Apiotrichum 1184.39462 0.735278334 0.33262155 2.210555332
## Exophiala 3276.55056 -0.113893840 0.11244333 -1.012899900
## NA. 5499.72627 -0.266086115 0.17017266 -1.563624351
## Aaosphaeria 2209.49427 0.001404389 0.23712799 0.005922492
## Pleotrichocladium 1228.03054 -0.246732117 0.24353919 -1.013110509
## Polyphilus 1015.79623 0.122375715 0.19369307 0.631802245
## Vishniacozyma 766.26376 0.859195545 0.25898552 3.317542830
## Anungitopsis 700.15888 -0.141167271 0.13627747 -1.035881236
## Cystobasidium 423.89482 0.014674994 0.35558944 0.041269489
## Herpotrichia 1784.66557 0.343009347 0.56174244 0.610616760
## Dioszegia 776.03696 1.412501022 0.50024785 2.823602412
## Boreoplaca 1100.10727 1.625276241 0.31549585 5.151497958
## Epicoccum 1171.99679 1.037309278 0.40750798 2.545494361
## Minimedusa 3376.06624 0.266690354 0.25753461 1.035551498
## Pseudeurotium 1512.71376 0.391183616 0.32992436 1.185676676
## Gorgomyces 426.09565 -0.988371202 0.35509022 -2.783436882
## Neoascochyta 377.90003 0.546276025 0.37093243 1.472710321
## Rhizophagus 1106.70653 0.298554887 0.30439906 0.980800960
## Umbelopsis 1187.02092 0.043999878 0.26503403 0.166015955
## Verrucula 374.76993 0.421711104 0.11501219 3.666664436
## Cystofilobasidium 3038.89858 1.990167444 0.55363080 3.594755632
## Olpidium 1036.85803 -1.214407794 0.55815189 -2.175765808
## Barnettozyma 591.32654 0.375901602 0.50843364 0.739332678
## Lambertella 988.62434 -0.390124959 0.20065390 -1.944267992
## Yunzhangia 345.19851 0.356066407 0.35140288 1.013271159
## Pseudogymnoascus 248.04469 -0.119729939 0.33608735 -0.356246488
## Crocicreas 186.96982 -0.209384360 0.46692977 -0.448427947
## Bullera 534.75171 1.417392235 0.45393514 3.122455446
## Waitea 144.14805 0.005147444 0.45269064 0.011370777
## Cladophialophora 286.52298 -0.036082125 0.40761705 -0.088519667
## Cadophora 523.10058 -0.882878199 0.20416232 -4.324393531
## Hamamotoa 200.47423 0.033836839 0.45217192 0.074831801
## Stromatinia 162.45048 0.482428813 0.35097822 1.374526362
## Galactomyces 142.75030 0.424632322 0.38808822 1.094164425
## Itersonilia 355.85415 1.626555802 0.35562550 4.573788403
## Knufia 289.11314 0.012580795 0.39208622 0.032086809
## Cutaneotrichosporon 146.60817 0.036707892 0.52795779 0.069528081
## Cladosporium 204.06860 0.836685957 0.33513887 2.496535102
## Chromosera 79.95274 -0.529805523 0.37203130 -1.424088590
## Rhodotorula 265.30898 1.169297348 0.41330993 2.829105369
## Atractiella 127.43030 -0.041339967 0.45658407 -0.090541852
## Funneliformis 173.97744 1.004949930 0.41280887 2.434419397
## Alternaria 114.82214 -0.327229518 0.31510939 -1.038463236
## Trichosporon 597.64206 0.557732733 0.38101737 1.463798714
## Rhizoctonia 214.33709 0.747246164 0.61251324 1.219967362
## Setophoma 77.48646 -1.196881984 0.36665321 -3.264343371
## Monodictys 83.97420 -0.630170569 0.37448172 -1.682780565
## Cantharellula 197.81389 -0.699373413 0.30905178 -2.262965160
## Paraglomus 117.01600 1.410850242 0.65735957 2.146238221
## Coprinellus 92.70154 -0.996851740 0.45364323 -2.197435488
## Pseudosigmoidea 100.80873 -0.105332522 0.32489559 -0.324204222
## Ganoderma 40.23399 -0.108813056 0.35932951 -0.302822485
## Cryptotrichosporon 86.47389 -1.239531075 0.41707480 -2.971963476
## Sporobolomyces 433.97502 0.868493893 0.32540557 2.668958322
## Filobasidium 162.05987 0.345731693 0.44425968 0.778219825
## Hyaloscypha 59.69257 -1.698611980 0.36960975 -4.595690393
## Laetinaevia 215.56526 0.048539437 0.30093676 0.161294477
## Paraboeremia 181.82217 0.012563635 0.30707594 0.040913772
## Lachnellula 72.61733 0.220247703 0.53297331 0.413243401
## Crassostrea 111.26717 0.909067503 0.32733500 2.777177817
## Hannaella 88.81468 -0.230277245 0.20822486 -1.105906599
## Pichia 159.95283 0.547036400 0.32341908 1.691416609
## Fusarium 120.37675 0.363078794 0.34250016 1.060083583
## Slooffia 46.28587 0.942862502 0.39337700 2.396841949
## Degelia 42.33919 -0.913593597 0.28366338 -3.220696249
## Varicosporium 47.19529 0.103580146 0.30483430 0.339791638
## Aotearoamyces 25.47022 0.608030309 0.28996993 2.096873653
## Boeremia 94.21359 1.045401610 0.55802848 1.873383965
## Sampaiozyma 80.61828 0.397577050 0.47806504 0.831637989
## Coniochaeta 57.76716 -0.177008246 0.40718654 -0.434710453
## Claroideoglomus 93.40685 0.588468302 0.25426341 2.314404195
## Entoloma 66.92444 -0.160796858 0.26299201 -0.611413463
## Archaeospora 46.88894 0.312411209 0.38832908 0.804501203
## Pulvinula 46.31055 -0.014919876 0.28655940 -0.052065562
## pvalue padj reject df
## Mrakia 1.957979e-01 0.41659120 FALSE 16
## Candida 3.583854e-01 0.55997716 FALSE 16
## Sistotrema 7.119734e-01 0.86826029 FALSE 16
## Bactrocera 1.363066e-01 0.32730173 FALSE 16
## Mortierella 6.812867e-01 0.84557241 FALSE 16
## Sporidiobolus 1.666677e-04 0.00780430 TRUE 16
## Juxtiphoma 9.208975e-01 0.99469356 FALSE 16
## Tausonia 6.509912e-01 0.84557241 FALSE 16
## Naganishia 5.903332e-02 0.16866664 FALSE 16
## Saitozyma 5.946949e-03 0.04955791 TRUE 16
## Botrytis 4.102506e-02 0.13890315 FALSE 16
## Solicoccozyma 8.429182e-01 0.96887148 FALSE 16
## Holtermanniella 3.805520e-01 0.58273252 FALSE 16
## Tetracladium 1.385568e-03 0.02309280 TRUE 16
## Ascobolus 2.913986e-01 0.54363551 FALSE 16
## Trichosporiella 2.994949e-02 0.11519036 FALSE 16
## Dendryphion 2.031486e-02 0.09673741 FALSE 16
## Papiliotrema 3.846035e-01 0.58273252 FALSE 16
## Incrupila 8.805964e-03 0.05993242 FALSE 16
## Paraphoma 2.442023e-01 0.49837201 FALSE 16
## Paraphaeosphaeria 3.481002e-01 0.55253995 FALSE 16
## Didymella 4.779042e-01 0.66751979 FALSE 16
## Haplosporangium 3.340222e-01 0.54757732 FALSE 16
## X.uncultured.fungus. 4.806143e-01 0.66751979 FALSE 16
## Burgoa 7.162601e-02 0.19358380 FALSE 16
## Pseudoanguillospora 6.805409e-01 0.84557241 FALSE 16
## Apiotrichum 4.197762e-02 0.13890315 FALSE 16
## Exophiala 3.261813e-01 0.54363551 FALSE 16
## NA. 1.374667e-01 0.32730173 FALSE 16
## Aaosphaeria 9.953478e-01 0.99534778 FALSE 16
## Pleotrichocladium 3.260838e-01 0.54363551 FALSE 16
## Polyphilus 5.364397e-01 0.73337271 FALSE 16
## Vishniacozyma 4.355308e-03 0.04839231 TRUE 16
## Anungitopsis 3.156609e-01 0.54363551 FALSE 16
## Cystobasidium 9.675917e-01 0.99469356 FALSE 16
## Herpotrichia 5.500295e-01 0.73337271 FALSE 16
## Dioszegia 1.223066e-02 0.07084152 FALSE 16
## Boreoplaca 9.653983e-05 0.00780430 TRUE 16
## Epicoccum 2.159908e-02 0.09817763 FALSE 16
## Minimedusa 3.158101e-01 0.54363551 FALSE 16
## Pseudeurotium 2.530607e-01 0.50612141 FALSE 16
## Gorgomyces 1.328745e-02 0.07084152 FALSE 16
## Neoascochyta 1.602279e-01 0.36958745 FALSE 16
## Rhizophagus 3.412898e-01 0.55046743 FALSE 16
## Umbelopsis 8.702241e-01 0.98188829 FALSE 16
## Verrucula 2.084051e-03 0.02977215 TRUE 16
## Cystofilobasidium 2.425943e-03 0.03032428 TRUE 16
## Olpidium 4.490436e-02 0.14032611 FALSE 16
## Barnettozyma 4.704137e-01 0.66751979 FALSE 16
## Lambertella 6.966079e-02 0.19350221 FALSE 16
## Yunzhangia 3.260094e-01 0.54363551 FALSE 16
## Pseudogymnoascus 7.263101e-01 0.87507238 FALSE 16
## Crocicreas 6.598576e-01 0.84557241 FALSE 16
## Bullera 6.563640e-03 0.05048954 FALSE 16
## Waitea 9.910682e-01 0.99534778 FALSE 16
## Cladophialophora 9.305622e-01 0.99469356 FALSE 16
## Cadophora 5.233783e-04 0.01046757 TRUE 16
## Hamamotoa 9.412762e-01 0.99469356 FALSE 16
## Stromatinia 1.882254e-01 0.40918566 FALSE 16
## Galactomyces 2.900821e-01 0.54363551 FALSE 16
## Itersonilia 3.121720e-04 0.00780430 TRUE 16
## Knufia 9.747997e-01 0.99469356 FALSE 16
## Cutaneotrichosporon 9.454308e-01 0.99469356 FALSE 16
## Cladosporium 2.383980e-02 0.10365132 FALSE 16
## Chromosera 1.736316e-01 0.38584796 FALSE 16
## Rhodotorula 1.209238e-02 0.07084152 FALSE 16
## Atractiella 9.289804e-01 0.99469356 FALSE 16
## Funneliformis 2.700101e-02 0.11250421 FALSE 16
## Alternaria 3.144943e-01 0.54363551 FALSE 16
## Trichosporon 1.626185e-01 0.36958745 FALSE 16
## Rhizoctonia 2.401549e-01 0.49837201 FALSE 16
## Setophoma 4.871706e-03 0.04854770 TRUE 16
## Monodictys 1.118259e-01 0.27956467 FALSE 16
## Cantharellula 3.790077e-02 0.13535990 FALSE 16
## Paraglomus 4.753503e-02 0.14404555 FALSE 16
## Coprinellus 4.305998e-02 0.13890315 FALSE 16
## Pseudosigmoidea 7.499831e-01 0.88233311 FALSE 16
## Ganoderma 7.659267e-01 0.89061246 FALSE 16
## Cryptotrichosporon 8.989864e-03 0.05993242 FALSE 16
## Sporobolomyces 1.680599e-02 0.08402996 FALSE 16
## Filobasidium 4.477957e-01 0.64897927 FALSE 16
## Hyaloscypha 2.983997e-04 0.00780430 TRUE 16
## Laetinaevia 8.738806e-01 0.98188829 FALSE 16
## Paraboeremia 9.678708e-01 0.99469356 FALSE 16
## Lachnellula 6.849136e-01 0.84557241 FALSE 16
## Crassostrea 1.345989e-02 0.07084152 FALSE 16
## Hannaella 2.851188e-01 0.54363551 FALSE 16
## Pichia 1.101390e-01 0.27956467 FALSE 16
## Fusarium 3.048479e-01 0.54363551 FALSE 16
## Slooffia 2.910159e-02 0.11519036 FALSE 16
## Degelia 5.340247e-03 0.04854770 TRUE 16
## Varicosporium 7.384331e-01 0.87908697 FALSE 16
## Aotearoamyces 5.225156e-02 0.15368105 FALSE 16
## Boeremia 7.940044e-02 0.20894852 FALSE 16
## Sampaiozyma 4.178527e-01 0.62366068 FALSE 16
## Coniochaeta 6.695787e-01 0.84557241 FALSE 16
## Claroideoglomus 3.426050e-02 0.12689075 FALSE 16
## Entoloma 5.495151e-01 0.73337271 FALSE 16
## Archaeospora 4.328997e-01 0.63661713 FALSE 16
## Pulvinula 9.591210e-01 0.99469356 FALSE 16
##
##
## $covariance
## NULL
##
## $feature.dat.use
## 1 2 3 4
## Mrakia 4.863046e-01 4.417461e-01 4.333489e-01 3.948711e-01
## Candida 3.535210e-02 2.892206e-02 1.839322e-02 2.370730e-02
## Sistotrema 5.102202e-02 1.738643e-02 6.272360e-02 6.080056e-02
## Bactrocera 1.674407e-03 9.803210e-03 1.736921e-03 3.136881e-03
## Mortierella 1.907876e-01 1.282301e-01 1.200938e-01 2.170519e-01
## Sporidiobolus 1.579629e-03 2.184715e-02 4.225644e-03 1.257643e-03
## Juxtiphoma 1.747070e-02 3.965891e-02 5.552963e-02 2.769707e-02
## Tausonia 1.766025e-02 3.539529e-02 4.097319e-02 2.068607e-02
## Naganishia 1.307933e-02 2.994906e-02 1.884689e-02 1.877792e-02
## Saitozyma 1.980855e-02 2.314387e-02 2.386322e-02 1.711552e-02
## Botrytis 2.748555e-03 1.705447e-02 3.694198e-03 4.784827e-03
## Solicoccozyma 3.383566e-02 3.713809e-02 3.962514e-02 3.798951e-02
## Holtermanniella 9.920071e-03 1.375561e-02 9.501218e-03 7.314570e-03
## Tetracladium 2.293621e-02 6.857060e-03 2.897029e-02 3.389855e-02
## Ascobolus 3.759517e-03 2.904655e-03 9.138280e-03 6.100293e-03
## Trichosporiella 1.295296e-02 6.857060e-03 1.893763e-02 1.520737e-02
## Dendryphion 2.590592e-03 1.794662e-03 6.053300e-03 3.845209e-03
## Papiliotrema 8.214071e-03 1.143189e-02 8.166122e-03 9.381731e-03
## Incrupila 7.361072e-03 2.759422e-03 5.807020e-03 1.464360e-02
## Paraphoma 2.274666e-03 1.141114e-04 1.970239e-03 5.652168e-03
## Paraphaeosphaeria 2.527407e-03 4.886044e-03 8.904962e-03 7.329026e-03
## Didymella 6.634442e-04 1.192983e-03 1.944315e-03 2.240629e-03
## Haplosporangium 1.421666e-03 2.738674e-03 4.523772e-03 4.669182e-03
## X.uncultured.fungus. 1.105740e-02 1.253151e-02 1.198994e-02 1.512063e-02
## Burgoa 7.898146e-04 5.083146e-04 9.591953e-04 4.914928e-04
## Pseudoanguillospora 6.318516e-04 3.008392e-04 2.359102e-03 1.301010e-03
## Apiotrichum 4.107036e-04 2.686806e-03 1.425831e-04 3.903031e-04
## Exophiala 1.895555e-03 1.670176e-03 4.964484e-03 4.336702e-03
## NA. 9.540960e-03 2.136996e-03 7.466169e-03 2.124984e-03
## Aaosphaeria 2.274666e-03 4.564457e-04 1.024006e-03 2.009338e-03
## Pleotrichocladium 4.107036e-04 9.855078e-04 2.786851e-03 1.272099e-03
## Polyphilus 4.107036e-04 6.120522e-04 1.348058e-03 3.613918e-04
## Vishniacozyma 9.477775e-05 1.628681e-03 3.759009e-04 2.457464e-04
## Anungitopsis 3.475184e-04 2.282229e-04 9.980816e-04 8.962516e-04
## Cystobasidium 9.477775e-04 7.365374e-04 2.722041e-04 1.734681e-04
## Herpotrichia 3.791110e-04 2.450284e-02 3.759009e-04 1.893693e-03
## Dioszegia 2.168351e-05 6.535473e-04 2.009125e-03 4.336702e-05
## Boreoplaca 5.782269e-05 6.016785e-04 7.518017e-04 1.156454e-04
## Epicoccum 7.459569e-05 8.796954e-03 5.314461e-04 2.023794e-04
## Minimedusa 4.644110e-03 2.686806e-03 2.203557e-03 1.069720e-03
## Pseudeurotium 1.579629e-03 5.913047e-04 6.351428e-04 1.156454e-03
## Gorgomyces 1.611222e-03 2.697179e-04 5.055219e-04 2.255085e-03
## Neoascochyta 6.002591e-04 2.800917e-04 3.759009e-04 5.782269e-05
## Rhizophagus 9.883376e-05 1.078872e-03 1.114740e-03 2.023794e-03
## Umbelopsis 1.705999e-03 6.742948e-04 5.703323e-04 8.817960e-04
## Verrucula 8.394614e-05 2.489704e-04 1.814694e-04 3.613918e-04
## Cystofilobasidium 1.579629e-04 3.459651e-02 1.685073e-04 7.898146e-05
## Olpidium 3.190851e-03 4.564457e-04 1.186032e-02 2.934501e-03
## Barnettozyma 3.791110e-04 6.639211e-04 4.605748e-05 4.605748e-05
## Lambertella 1.042555e-03 5.809309e-04 1.568414e-03 1.445567e-03
## Yunzhangia 8.529997e-04 3.215868e-04 1.425831e-04 4.858352e-05
## Pseudogymnoascus 4.738887e-04 2.074753e-04 2.592420e-04 3.469361e-04
## Crocicreas 2.843332e-04 9.336390e-05 4.277493e-04 3.758475e-04
## Bullera 3.971721e-05 8.506489e-04 1.296210e-04 1.416656e-03
## Waitea 2.211481e-04 4.149507e-05 1.944315e-04 7.227836e-05
## Cladophialophora 3.791110e-04 1.244852e-04 2.203557e-04 7.806063e-04
## Cadophora 1.137333e-03 1.214168e-04 1.101778e-03 1.792503e-03
## Hamamotoa 1.895555e-04 4.356982e-04 1.036968e-03 8.673403e-05
## Stromatinia 2.527407e-04 1.348590e-04 3.306550e-05 3.306550e-05
## Galactomyces 2.593442e-05 5.186883e-05 9.073469e-05 8.673403e-05
## Itersonilia 4.536735e-05 4.149507e-04 9.073469e-05 4.536735e-05
## Knufia 4.422961e-04 1.556065e-04 5.962565e-04 5.203575e-05
## Cutaneotrichosporon 2.527407e-04 6.327998e-04 1.685073e-04 1.445567e-04
## Cladosporium 4.354967e-05 3.527081e-04 1.166589e-04 4.354967e-05
## Chromosera 1.579629e-04 3.112130e-05 9.073469e-05 2.891134e-04
## Rhodotorula 5.398169e-05 2.074753e-04 5.398169e-05 5.398169e-05
## Atractiella 3.159258e-04 7.261637e-05 2.699084e-05 2.699084e-05
## Funneliformis 3.112130e-05 6.224260e-05 3.112130e-05 3.112130e-05
## Alternaria 3.260681e-05 3.260681e-05 1.296210e-04 1.734681e-04
## Trichosporon 3.159258e-04 9.855078e-04 1.579629e-04 1.579629e-04
## Rhizoctonia 3.349590e-05 1.452327e-04 9.462332e-04 3.469361e-04
## Setophoma 1.263703e-04 4.149507e-05 1.036968e-04 2.081617e-03
## Monodictys 2.312700e-05 1.659803e-04 3.110904e-04 2.168351e-04
## Cantharellula 1.200518e-03 6.029262e-05 4.018251e-04 1.011897e-03
## Paraglomus 1.156350e-05 1.156350e-05 1.156350e-05 1.156454e-04
## Coprinellus 2.211481e-04 1.491914e-05 1.491914e-05 1.734681e-04
## Pseudosigmoidea 3.294459e-05 1.659803e-04 3.294459e-05 3.469361e-04
## Ganoderma 1.037377e-05 2.074753e-05 3.888630e-05 1.037377e-05
## Cryptotrichosporon 4.107036e-04 2.282229e-04 1.166589e-04 3.469361e-04
## Sporobolomyces 1.381724e-04 6.950424e-04 1.381724e-04 1.381724e-04
## Filobasidium 9.477775e-05 4.149507e-04 4.475741e-05 4.475741e-05
## Hyaloscypha 1.895555e-04 1.156350e-05 4.018251e-04 1.301010e-04
## Laetinaevia 8.038954e-05 4.253244e-04 4.277493e-04 3.180248e-04
## Paraboeremia 2.843332e-04 6.588917e-05 6.588917e-05 4.336702e-04
## Lachnellula 4.107036e-04 1.445567e-05 9.073469e-05 2.891134e-05
## Crassostrea 3.349590e-05 3.349590e-05 3.349590e-05 3.349590e-05
## Hannaella 3.732829e-05 3.732829e-05 9.073469e-05 1.734681e-04
## Pichia 5.796765e-05 9.232652e-04 1.814694e-04 5.796765e-05
## Fusarium 4.477345e-05 2.904655e-04 1.425831e-04 8.384290e-04
## Slooffia 1.296210e-05 1.296210e-05 2.592420e-05 1.296210e-05
## Degelia 1.263703e-04 8.299013e-05 5.184840e-05 1.734681e-04
## Varicosporium 1.811489e-05 1.037377e-04 1.555452e-04 1.734681e-04
## Aotearoamyces 9.949655e-06 3.112130e-05 7.777259e-05 9.949655e-06
## Boeremia 2.159268e-05 3.215868e-04 2.159268e-05 2.159268e-05
## Sampaiozyma 2.487414e-05 8.299013e-05 2.487414e-05 7.661506e-04
## Coniochaeta 1.989931e-05 1.141114e-04 6.481049e-05 3.758475e-04
## Claroideoglomus 4.149507e-05 8.299013e-05 4.149507e-05 4.149507e-05
## Entoloma 1.579629e-04 2.978791e-05 2.978791e-05 2.978791e-05
## Archaeospora 1.678923e-05 1.678923e-05 7.777259e-05 1.678923e-05
## Pulvinula 2.009754e-05 2.009754e-05 7.777259e-05 2.009754e-05
## 5 6 7 8
## Mrakia 3.878983e-01 3.420844e-01 3.388772e-01 3.387759e-01
## Candida 4.793325e-02 2.142744e-02 4.334255e-02 2.659605e-02
## Sistotrema 4.343373e-02 1.455636e-02 7.296035e-02 1.490535e-02
## Bactrocera 3.508949e-03 6.116451e-03 3.264392e-03 9.909920e-03
## Mortierella 1.758168e-01 2.692628e-01 1.668176e-01 1.421501e-01
## Sporidiobolus 2.367281e-03 1.153785e-02 1.501144e-03 2.911690e-02
## Juxtiphoma 3.384708e-02 3.171419e-02 5.089592e-02 6.247760e-02
## Tausonia 2.778617e-02 2.615379e-02 2.725886e-02 6.761179e-02
## Naganishia 2.300124e-02 2.889427e-02 1.348647e-02 2.238694e-02
## Saitozyma 3.042208e-02 3.417666e-02 3.628955e-02 2.543970e-02
## Botrytis 3.139586e-03 3.713559e-03 1.048418e-02 2.607569e-02
## Solicoccozyma 4.479366e-02 5.179125e-02 4.870377e-02 4.841638e-02
## Holtermanniella 1.061079e-02 1.342442e-02 1.103222e-02 2.843465e-02
## Tetracladium 2.810517e-02 1.263007e-02 2.073008e-02 1.696366e-02
## Ascobolus 2.719855e-03 2.859639e-03 6.195196e-03 1.084656e-02
## Trichosporiella 1.259192e-02 9.810152e-03 1.748952e-02 1.408434e-02
## Dendryphion 4.566670e-03 3.097943e-03 5.551849e-03 2.867748e-03
## Papiliotrema 1.235687e-02 1.062435e-02 7.434236e-03 6.498688e-03
## Incrupila 1.170209e-02 5.361824e-03 8.625620e-03 2.358954e-03
## Paraphoma 3.223532e-03 9.929303e-05 1.667937e-03 6.417743e-03
## Paraphaeosphaeria 8.931869e-03 7.685281e-03 1.122284e-02 6.718394e-03
## Didymella 9.234075e-04 3.455398e-03 9.769348e-04 4.417257e-03
## Haplosporangium 2.904536e-03 9.849869e-03 5.623332e-03 4.995432e-03
## X.uncultured.fungus. 1.764548e-02 1.622448e-02 1.596454e-02 7.516276e-03
## Burgoa 6.547799e-04 9.730717e-04 5.718643e-04 3.700320e-04
## Pseudoanguillospora 4.700984e-04 8.142029e-04 5.742470e-03 1.537946e-03
## Apiotrichum 1.208824e-03 5.480975e-03 4.527259e-04 6.568069e-03
## Exophiala 2.417649e-03 3.296529e-03 3.312047e-03 2.081430e-03
## NA. 5.439710e-03 4.150449e-03 8.434998e-03 3.191526e-03
## Aaosphaeria 1.947550e-03 2.164588e-03 2.883149e-03 4.394130e-04
## Pleotrichocladium 1.410295e-03 2.164588e-03 3.121426e-03 1.237295e-03
## Polyphilus 1.242403e-03 3.971721e-04 9.769348e-04 4.162860e-04
## Vishniacozyma 3.022061e-04 6.354754e-04 9.054518e-04 1.491692e-03
## Anungitopsis 5.372553e-04 5.361824e-04 1.096073e-03 4.047225e-04
## Cystobasidium 5.708338e-04 7.149098e-04 4.527259e-04 6.128656e-04
## Herpotrichia 2.182600e-03 9.015807e-03 9.245139e-03 1.965795e-04
## Dioszegia 5.708338e-04 5.957582e-05 1.191384e-04 1.680177e-02
## Boreoplaca 1.175246e-04 1.310668e-03 2.382768e-04 3.815955e-04
## Epicoccum 4.029415e-04 5.361824e-04 9.769348e-04 2.012049e-03
## Minimedusa 1.309560e-03 2.601477e-03 2.430423e-03 1.167914e-03
## Pseudeurotium 1.393506e-03 1.072365e-03 4.765536e-04 3.700320e-04
## Gorgomyces 3.928679e-03 2.978791e-04 9.531071e-05 2.890875e-04
## Neoascochyta 4.868876e-04 4.964652e-04 2.891134e-05 3.815955e-04
## Rhizophagus 9.234075e-04 1.548971e-03 2.096836e-03 6.475561e-04
## Umbelopsis 1.208824e-03 2.065295e-03 1.286695e-03 1.381724e-04
## Verrucula 1.678923e-04 3.971721e-04 4.527259e-04 3.122145e-04
## Cystofilobasidium 1.024143e-02 8.856939e-03 7.898146e-05 1.055748e-02
## Olpidium 5.204661e-04 8.737787e-04 1.119901e-03 3.353415e-04
## Barnettozyma 4.751352e-03 5.758996e-04 1.382005e-03 1.271985e-04
## Lambertella 1.091300e-03 1.886568e-03 1.596454e-03 1.260422e-03
## Yunzhangia 1.007354e-04 4.567480e-04 4.050705e-04 2.197065e-04
## Pseudogymnoascus 3.861522e-04 5.560410e-04 2.859321e-04 2.312700e-04
## Crocicreas 3.861522e-04 3.177377e-04 3.335875e-04 1.156350e-04
## Bullera 3.971721e-05 7.943443e-05 3.971721e-05 1.364493e-03
## Waitea 8.394614e-05 2.074753e-05 1.191384e-04 1.156350e-04
## Cladophialophora 6.883583e-04 4.567480e-04 3.469050e-05 6.938101e-05
## Cadophora 7.051476e-04 3.177377e-04 6.195196e-04 2.428335e-04
## Hamamotoa 3.294459e-05 3.375963e-04 2.096836e-03 6.591196e-04
## Stromatinia 3.306550e-05 8.737787e-04 7.148303e-05 1.503255e-04
## Galactomyces 2.182600e-04 5.361824e-04 5.003812e-04 3.931590e-04
## Itersonilia 4.536735e-05 4.536735e-05 4.536735e-05 9.597706e-04
## Knufia 3.693630e-04 5.203575e-05 7.624857e-04 1.040715e-04
## Cutaneotrichosporon 1.678923e-04 4.567480e-04 1.667937e-04 3.469050e-05
## Cladosporium 1.007354e-04 2.581619e-04 4.354967e-05 6.591196e-04
## Chromosera 2.854169e-04 1.191516e-04 1.556065e-05 1.556065e-05
## Rhodotorula 5.398169e-05 2.978791e-04 5.398169e-05 1.387620e-04
## Atractiella 2.699084e-05 1.588689e-04 3.478841e-03 2.197065e-04
## Funneliformis 3.112130e-05 9.929303e-05 4.050705e-04 1.040715e-04
## Alternaria 2.014707e-04 3.260681e-05 1.429661e-04 4.162860e-04
## Trichosporon 1.579629e-04 8.142029e-04 8.673275e-03 1.942668e-03
## Rhizoctonia 3.349590e-05 3.349590e-05 2.621045e-04 9.250801e-05
## Setophoma 7.219368e-04 2.074753e-05 4.765536e-05 2.074753e-05
## Monodictys 1.175246e-04 2.312700e-05 1.191384e-04 4.625400e-05
## Cantharellula 1.343138e-04 3.177377e-04 3.812429e-04 1.271985e-04
## Paraglomus 1.156350e-05 1.156350e-05 1.191384e-04 2.312700e-05
## Coprinellus 3.357846e-04 3.574549e-04 1.906214e-04 8.094451e-05
## Pseudosigmoidea 3.022061e-04 3.294459e-05 3.294459e-05 1.850160e-04
## Ganoderma 1.511031e-04 9.929303e-05 1.191384e-04 9.250801e-05
## Cryptotrichosporon 2.048286e-03 3.971721e-05 1.429661e-04 3.700320e-04
## Sporobolomyces 1.381724e-04 1.381724e-04 1.381724e-04 4.741035e-04
## Filobasidium 4.475741e-05 2.184447e-04 8.101411e-04 1.376057e-03
## Hyaloscypha 7.387260e-04 7.943443e-05 1.191384e-04 2.312700e-05
## Laetinaevia 1.678923e-04 4.964652e-04 8.038954e-05 7.631911e-04
## Paraboeremia 6.588917e-05 6.588917e-05 6.588917e-05 6.588917e-05
## Lachnellula 1.445567e-05 1.191516e-04 1.445567e-05 1.445567e-05
## Crassostrea 3.349590e-05 1.588689e-04 7.148303e-05 3.349590e-05
## Hannaella 1.175246e-04 7.943443e-05 2.144491e-04 2.081430e-04
## Pichia 5.796765e-05 5.796765e-05 5.796765e-05 5.796765e-05
## Fusarium 4.477345e-05 2.184447e-04 4.477345e-05 6.591196e-04
## Slooffia 1.296210e-05 2.780205e-04 1.296210e-05 1.296210e-05
## Degelia 1.678923e-04 1.492448e-05 1.906214e-04 3.469050e-05
## Varicosporium 1.811489e-05 1.811489e-05 1.811489e-05 9.250801e-05
## Aotearoamyces 9.949655e-06 9.949655e-06 9.949655e-06 3.469050e-05
## Boeremia 2.159268e-05 2.159268e-05 2.159268e-05 3.353415e-04
## Sampaiozyma 2.487414e-05 2.487414e-05 1.906214e-04 2.543970e-04
## Coniochaeta 2.518384e-04 3.177377e-04 1.989931e-05 1.271985e-04
## Claroideoglomus 4.149507e-05 4.149507e-05 4.149507e-05 1.387620e-04
## Entoloma 2.978791e-05 5.957582e-05 2.144491e-04 2.978791e-05
## Archaeospora 3.357846e-05 1.678923e-05 9.531071e-05 1.678923e-05
## Pulvinula 2.009754e-05 1.985861e-04 1.667937e-04 2.009754e-05
## 9 10 11 12
## Mrakia 2.348949e-01 3.194431e-01 2.877716e-01 2.602708e-01
## Candida 3.379126e-01 1.420964e-02 7.338417e-02 1.522200e-02
## Sistotrema 1.086512e-01 1.037229e-01 5.190868e-02 4.971905e-02
## Bactrocera 1.251894e-03 1.928628e-03 4.012587e-02 2.371960e-02
## Mortierella 7.853990e-02 2.051563e-01 1.469291e-01 1.632507e-01
## Sporidiobolus 1.103644e-02 4.852678e-03 3.312049e-02 1.508382e-02
## Juxtiphoma 2.658628e-02 4.308929e-02 2.161330e-02 8.235077e-02
## Tausonia 1.083877e-02 1.873880e-02 2.680417e-02 4.789978e-02
## Naganishia 2.141398e-02 1.067589e-02 6.304837e-02 1.027082e-02
## Saitozyma 2.642156e-02 3.215210e-02 2.995085e-02 2.001197e-02
## Botrytis 7.840812e-03 5.366564e-02 7.556617e-03 7.039886e-02
## Solicoccozyma 2.302827e-02 3.870944e-02 3.539437e-02 4.069178e-02
## Holtermanniella 8.400870e-03 4.790464e-03 1.796132e-02 8.935151e-03
## Tetracladium 3.525071e-03 2.627912e-02 6.017732e-03 3.369105e-02
## Ascobolus 3.525071e-03 4.093669e-03 4.639625e-03 1.667281e-02
## Trichosporiella 6.358305e-03 9.817340e-03 5.282742e-03 1.358696e-02
## Dendryphion 7.577255e-04 2.724965e-03 1.837475e-03 1.360999e-02
## Papiliotrema 1.311195e-02 6.320924e-03 2.133768e-02 4.053058e-03
## Incrupila 3.162680e-03 9.244973e-03 2.664339e-03 6.655306e-03
## Paraphoma 1.647229e-04 3.658172e-03 5.971795e-04 2.003500e-03
## Paraphaeosphaeria 2.734401e-03 5.313060e-03 3.238550e-03 5.296610e-03
## Didymella 3.294459e-04 1.567788e-03 7.809270e-04 1.215917e-02
## Haplosporangium 3.096791e-03 4.193211e-03 2.825118e-03 3.799742e-03
## X.uncultured.fungus. 1.202477e-02 1.011597e-02 1.146125e-02 1.066231e-02
## Burgoa 8.071424e-03 2.550766e-03 1.584822e-03 7.599484e-04
## Pseudoanguillospora 3.953350e-03 8.958790e-04 2.526529e-04 1.612012e-03
## Apiotrichum 9.224484e-04 4.354967e-04 2.159033e-03 1.427782e-03
## Exophiala 2.009620e-03 3.160462e-03 1.309201e-03 5.273581e-03
## NA. 5.831192e-03 2.376568e-03 2.204970e-03 8.566691e-03
## Aaosphaeria 2.372010e-03 2.961378e-03 1.791538e-03 9.211496e-04
## Pleotrichocladium 3.953350e-04 1.605116e-03 3.904635e-04 2.740420e-03
## Polyphilus 9.553930e-04 1.368704e-03 5.282742e-04 9.902358e-04
## Vishniacozyma 4.282796e-04 2.115270e-04 4.111351e-03 7.138909e-04
## Anungitopsis 3.623905e-04 8.834362e-04 5.053057e-04 1.082351e-03
## Cystobasidium 3.953350e-04 7.465658e-05 1.171391e-03 1.151437e-04
## Herpotrichia 3.689794e-03 4.106112e-04 1.527401e-02 9.441783e-04
## Dioszegia 6.588917e-04 5.848099e-04 2.848087e-03 5.526898e-04
## Boreoplaca 1.680174e-03 2.264583e-03 4.685562e-03 5.987472e-04
## Epicoccum 2.306121e-04 4.417181e-03 3.606045e-03 1.842299e-03
## Minimedusa 4.216907e-03 4.504280e-03 5.348599e-04 3.270081e-03
## Pseudeurotium 1.548396e-03 2.077941e-03 1.745602e-03 1.850160e-04
## Gorgomyces 3.349590e-05 8.087796e-04 4.364004e-04 3.914886e-04
## Neoascochyta 6.588917e-04 2.364125e-04 7.349901e-04 1.381724e-04
## Rhizophagus 1.976675e-04 3.857257e-03 5.282742e-04 7.369197e-04
## Umbelopsis 1.054227e-03 1.717101e-03 1.148422e-03 2.763449e-04
## Verrucula 2.306121e-04 4.603822e-04 3.215582e-04 4.375461e-04
## Cystofilobasidium 1.910786e-03 8.709934e-04 3.256925e-02 4.836035e-04
## Olpidium 4.593688e-05 8.585507e-04 9.187377e-05 1.476142e-02
## Barnettozyma 6.259472e-04 2.861836e-04 2.733245e-03 9.211496e-05
## Lambertella 3.953350e-04 1.070078e-03 4.823373e-04 1.473839e-03
## Yunzhangia 1.087171e-03 3.732829e-04 2.296844e-04 4.375461e-04
## Pseudogymnoascus 3.306550e-05 4.354967e-04 2.756213e-04 3.306550e-05
## Crocicreas 6.259472e-04 2.488553e-05 1.244276e-05 1.244276e-05
## Bullera 4.612242e-04 5.350388e-04 5.121962e-03 5.987472e-04
## Waitea 1.976675e-04 2.074753e-05 6.890532e-05 2.302874e-04
## Cladophialophora 1.647229e-04 8.834362e-04 8.498323e-04 3.469050e-05
## Cadophora 1.214168e-04 7.714513e-04 1.214168e-04 1.519897e-03
## Hamamotoa 6.588917e-05 3.294459e-05 2.526529e-04 1.612012e-04
## Stromatinia 1.647229e-04 2.861836e-04 1.837475e-04 5.296610e-04
## Galactomyces 8.565593e-04 8.709934e-05 9.187377e-05 2.593442e-05
## Itersonilia 9.224484e-04 7.465658e-04 7.349901e-04 1.842299e-04
## Knufia 5.203575e-05 1.032749e-03 4.134319e-04 5.203575e-05
## Cutaneotrichosporon 4.282796e-04 1.734525e-05 3.904635e-04 1.734525e-05
## Cladosporium 4.282796e-04 8.709934e-05 2.756213e-04 2.763449e-04
## Chromosera 6.588917e-05 8.709934e-05 1.378106e-04 4.605748e-05
## Rhodotorula 4.612242e-04 3.608401e-04 6.201479e-04 2.993736e-04
## Atractiella 2.699084e-05 1.368704e-04 9.187377e-05 3.454311e-04
## Funneliformis 3.112130e-05 4.106112e-04 9.417061e-04 1.612012e-04
## Alternaria 3.260681e-05 9.954211e-05 3.260681e-05 6.217760e-04
## Trichosporon 6.918363e-04 5.101533e-04 1.194359e-03 1.579629e-04
## Rhizoctonia 3.349590e-05 1.356261e-03 3.349590e-05 3.349590e-05
## Setophoma 2.074753e-05 1.244276e-04 2.074753e-05 4.605748e-05
## Monodictys 6.588917e-05 6.221382e-05 2.312700e-05 1.635041e-03
## Cantharellula 6.029262e-05 2.364125e-04 6.029262e-05 6.029262e-05
## Paraglomus 4.282796e-04 1.318933e-03 3.904635e-04 1.156350e-05
## Coprinellus 1.491914e-05 5.599243e-04 1.491914e-05 7.829772e-04
## Pseudosigmoidea 6.588917e-05 1.866414e-04 1.148422e-04 2.533161e-04
## Ganoderma 1.037377e-05 3.732829e-05 1.037377e-05 6.908622e-05
## Cryptotrichosporon 1.985861e-05 1.617559e-04 1.985861e-05 4.605748e-05
## Sporobolomyces 1.381724e-04 1.381724e-04 1.584822e-03 2.763449e-04
## Filobasidium 1.647229e-04 4.475741e-05 2.756213e-04 7.829772e-04
## Hyaloscypha 1.156350e-05 1.156350e-05 1.156350e-05 2.993736e-04
## Laetinaevia 8.038954e-05 2.239697e-04 1.607791e-04 6.908622e-04
## Paraboeremia 1.317783e-04 5.599243e-04 2.067160e-04 2.533161e-04
## Lachnellula 1.647229e-04 1.445567e-05 3.215582e-04 1.445567e-05
## Crassostrea 2.965013e-04 1.617559e-04 3.349590e-05 3.349590e-05
## Hannaella 3.732829e-05 7.465658e-05 3.732829e-05 1.151437e-04
## Pichia 5.796765e-05 4.852678e-04 4.593688e-04 3.914886e-04
## Fusarium 4.477345e-05 1.368704e-04 4.477345e-05 4.477345e-05
## Slooffia 6.588917e-05 4.977105e-05 1.296210e-05 1.296210e-05
## Degelia 1.492448e-05 1.492448e-05 1.492448e-05 1.492448e-05
## Varicosporium 1.811489e-05 7.465658e-05 1.811489e-05 6.908622e-05
## Aotearoamyces 6.588917e-05 3.732829e-05 6.890532e-05 9.949655e-06
## Boeremia 2.159268e-05 5.300617e-03 1.561854e-03 2.302874e-04
## Sampaiozyma 2.487414e-05 9.954211e-05 7.579586e-04 2.487414e-05
## Coniochaeta 1.989931e-05 1.989931e-05 1.989931e-05 1.989931e-05
## Claroideoglomus 4.149507e-05 1.741987e-04 4.149507e-05 1.151437e-04
## Entoloma 2.978791e-05 7.465658e-05 2.978791e-05 6.908622e-05
## Archaeospora 9.883376e-05 1.678923e-05 1.148422e-04 1.678923e-05
## Pulvinula 2.009754e-05 8.709934e-05 2.009754e-05 9.211496e-05
## 13 14 15 16
## Mrakia 2.197703e-01 1.847039e-01 2.393617e-01 2.380455e-01
## Candida 1.929306e-02 2.627716e-01 3.700627e-02 1.496428e-02
## Sistotrema 2.575358e-01 9.886585e-02 4.268718e-02 8.602471e-02
## Bactrocera 1.390568e-02 6.745363e-03 6.739375e-03 1.727260e-01
## Mortierella 1.984151e-01 8.134114e-02 1.768048e-01 1.229180e-01
## Sporidiobolus 3.749568e-02 4.708528e-02 5.094056e-02 1.085607e-01
## Juxtiphoma 1.825661e-02 2.218695e-02 3.506351e-02 1.724275e-02
## Tausonia 1.141173e-02 1.213504e-02 3.632295e-02 1.597915e-02
## Naganishia 1.482337e-02 3.746321e-02 3.774318e-02 3.936083e-02
## Saitozyma 2.937684e-02 3.085011e-02 3.731443e-02 2.474479e-02
## Botrytis 2.154949e-02 1.196971e-02 3.872126e-02 1.643683e-02
## Solicoccozyma 3.461306e-02 2.529511e-02 6.423174e-02 3.105287e-02
## Holtermanniella 3.843496e-03 8.464769e-03 2.486736e-02 9.302927e-03
## Tetracladium 8.809812e-03 2.579109e-03 1.548850e-02 5.442461e-03
## Ascobolus 1.425117e-03 1.950865e-03 4.716223e-03 1.452650e-03
## Trichosporiella 6.693729e-03 2.182323e-03 2.053969e-02 3.989812e-03
## Dendryphion 2.418380e-03 1.190358e-03 6.511603e-03 7.661234e-04
## Papiliotrema 4.912334e-03 8.167179e-03 1.677475e-02 7.661234e-03
## Incrupila 7.762567e-03 5.521939e-03 7.248513e-03 4.736036e-03
## Paraphoma 2.925808e-03 1.183745e-02 3.751541e-04 1.273556e-03
## Paraphaeosphaeria 4.156590e-03 1.388751e-03 7.114529e-03 3.362983e-03
## Didymella 8.852997e-04 3.306550e-04 5.091377e-04 5.273317e-04
## Haplosporangium 1.522284e-03 3.174288e-03 3.724744e-03 2.477464e-03
## X.uncultured.fungus. 1.510408e-02 3.356149e-02 1.006217e-02 9.402424e-03
## Burgoa 3.228105e-03 3.835598e-03 3.215606e-04 1.771039e-03
## Pseudoanguillospora 8.529107e-04 9.588996e-04 1.152259e-03 1.989931e-04
## Apiotrichum 3.454828e-04 1.818603e-03 1.768584e-03 2.885400e-04
## Exophiala 2.170064e-03 2.512978e-03 2.947639e-03 2.069528e-03
## NA. 3.422439e-03 6.315511e-03 5.613913e-03 5.213619e-03
## Aaosphaeria 3.238901e-03 2.116192e-03 1.728388e-03 1.601894e-03
## Pleotrichocladium 4.534462e-04 7.935721e-04 2.438502e-03 3.183890e-04
## Polyphilus 2.483158e-03 7.935721e-04 1.594405e-03 6.765765e-04
## Vishniacozyma 8.313180e-04 1.289555e-03 7.503082e-04 1.741190e-03
## Anungitopsis 9.284851e-04 4.298515e-04 5.225360e-04 5.472310e-04
## Cystobasidium 3.238901e-05 2.645240e-04 1.339836e-03 1.492448e-04
## Herpotrichia 9.828976e-05 3.504943e-03 1.085267e-03 3.880365e-04
## Dioszegia 2.699084e-04 2.215389e-03 2.036551e-03 3.681372e-03
## Boreoplaca 2.774659e-03 1.917799e-03 7.235114e-04 3.024695e-03
## Epicoccum 8.421144e-04 9.588996e-04 3.496972e-03 1.492448e-03
## Minimedusa 6.726119e-03 4.926760e-03 3.148615e-03 4.855432e-03
## Pseudeurotium 4.016238e-03 1.124227e-03 9.646819e-04 1.601894e-03
## Gorgomyces 4.858352e-04 1.322620e-04 6.699180e-05 1.492448e-04
## Neoascochyta 2.807048e-04 3.240419e-03 3.215606e-04 1.492448e-04
## Rhizophagus 2.018915e-03 2.645240e-04 1.219251e-03 7.860227e-04
## Umbelopsis 6.585766e-04 1.025031e-03 1.045072e-03 1.164110e-03
## Verrucula 4.318535e-04 3.306550e-04 3.081623e-04 3.581876e-04
## Cystofilobasidium 1.271809e-02 1.223424e-02 2.827054e-03 5.810598e-03
## Olpidium 3.562792e-04 1.653275e-04 1.574307e-02 4.593688e-05
## Barnettozyma 3.994645e-04 5.852594e-03 3.081623e-04 1.691441e-04
## Lambertella 5.182242e-04 1.976675e-04 1.393429e-03 1.976675e-04
## Yunzhangia 9.716704e-05 9.588996e-04 2.277721e-04 4.858352e-05
## Pseudogymnoascus 2.159268e-04 6.613101e-05 1.071869e-04 1.790938e-04
## Crocicreas 1.187597e-04 6.282446e-04 2.143738e-04 1.293455e-04
## Bullera 1.079634e-03 1.190358e-03 1.822177e-03 2.397867e-03
## Waitea 1.079634e-04 3.604140e-03 2.074753e-05 1.591945e-04
## Cladophialophora 3.454828e-04 3.469050e-05 2.679672e-04 5.969793e-04
## Cadophora 5.506132e-04 1.214168e-04 3.617557e-04 3.681372e-04
## Hamamotoa 1.079634e-04 3.294459e-05 5.225360e-04 8.954689e-05
## Stromatinia 4.102608e-04 6.613101e-05 1.741787e-04 3.306550e-05
## Galactomyces 2.593442e-05 2.645240e-04 1.875770e-04 2.593442e-05
## Itersonilia 1.295561e-04 1.256489e-03 7.637065e-04 3.124192e-03
## Knufia 3.346865e-04 5.951790e-04 5.203575e-05 3.880365e-04
## Cutaneotrichosporon 1.734525e-05 7.605066e-04 3.751541e-04 1.734525e-05
## Cladosporium 4.354967e-05 4.354967e-05 4.019508e-04 1.144210e-03
## Chromosera 1.556065e-05 1.556065e-05 1.556065e-05 1.094462e-04
## Rhodotorula 1.079634e-04 2.645240e-03 6.203441e-03 3.283386e-04
## Atractiella 5.398169e-05 3.306550e-04 2.699084e-05 2.699084e-05
## Funneliformis 4.210572e-04 1.388751e-03 3.112130e-05 1.989931e-04
## Alternaria 1.511487e-04 3.260681e-05 1.607803e-04 2.188924e-04
## Trichosporon 1.579629e-04 1.579629e-04 8.173000e-04 1.579629e-04
## Rhizoctonia 4.318535e-04 1.613597e-02 6.699180e-05 3.349590e-05
## Setophoma 2.074753e-05 2.074753e-05 5.359344e-05 2.188924e-04
## Monodictys 2.312700e-05 2.312700e-05 2.312700e-05 2.312700e-05
## Cantharellula 6.029262e-05 6.029262e-05 1.205852e-04 6.029262e-05
## Paraglomus 6.693729e-04 1.554079e-03 1.156350e-05 2.387917e-04
## Coprinellus 1.491914e-05 1.491914e-05 2.813656e-04 1.491914e-05
## Pseudosigmoidea 1.727414e-04 3.294459e-05 3.294459e-05 6.964758e-05
## Ganoderma 6.477803e-05 1.037377e-05 1.071869e-04 1.037377e-05
## Cryptotrichosporon 1.985861e-05 1.985861e-05 1.985861e-05 1.985861e-05
## Sporobolomyces 1.813785e-03 4.298515e-04 1.902567e-03 2.019780e-03
## Filobasidium 4.475741e-05 4.475741e-05 4.475741e-05 4.875331e-04
## Hyaloscypha 3.238901e-05 1.156350e-05 7.503082e-04 1.156350e-05
## Laetinaevia 8.038954e-05 8.038954e-05 2.545688e-04 8.038954e-05
## Paraboeremia 5.290206e-04 6.588917e-05 6.588917e-05 1.591945e-04
## Lachnellula 2.807048e-04 1.445567e-05 4.823410e-04 1.445567e-05
## Crassostrea 3.454828e-04 4.629170e-04 6.699180e-05 2.686407e-04
## Hannaella 3.732829e-05 3.732829e-05 1.607803e-04 3.732829e-05
## Pichia 2.699084e-04 3.306550e-04 5.796765e-05 5.796765e-05
## Fusarium 4.477345e-05 4.477345e-05 1.607803e-04 8.954689e-05
## Slooffia 1.296210e-05 3.306550e-04 1.741787e-04 1.492448e-04
## Degelia 1.492448e-05 1.492448e-05 6.699180e-05 2.984896e-05
## Varicosporium 1.811489e-05 1.811489e-05 1.811489e-05 5.969793e-05
## Aotearoamyces 9.949655e-06 9.949655e-06 9.949655e-06 1.989931e-05
## Boeremia 4.318535e-05 2.159268e-05 2.143738e-04 2.159268e-05
## Sampaiozyma 2.487414e-05 7.935721e-04 2.487414e-05 4.974827e-05
## Coniochaeta 1.989931e-05 1.989931e-05 1.989931e-05 3.979862e-05
## Claroideoglomus 1.403524e-04 1.983930e-04 4.149507e-05 3.382883e-04
## Entoloma 2.978791e-05 2.978791e-05 2.679672e-04 2.978791e-05
## Archaeospora 2.483158e-04 1.678923e-05 1.678923e-05 1.691441e-04
## Pulvinula 2.009754e-05 2.009754e-05 4.019508e-05 4.974827e-05
## 17 18
## Mrakia 1.761057e-01 1.168765e-01
## Candida 3.755579e-02 5.599650e-02
## Sistotrema 1.718886e-01 1.681387e-01
## Bactrocera 2.325952e-03 1.287024e-02
## Mortierella 2.290809e-01 2.376519e-01
## Sporidiobolus 3.520086e-02 2.538243e-02
## Juxtiphoma 2.935337e-02 4.684609e-02
## Tausonia 8.615443e-03 1.155736e-02
## Naganishia 1.268767e-02 2.152334e-02
## Saitozyma 4.953336e-02 5.331105e-02
## Botrytis 3.433134e-02 8.185634e-03
## Solicoccozyma 4.683062e-02 4.034135e-02
## Holtermanniella 1.078923e-02 8.275149e-03
## Tetracladium 5.970668e-03 6.146685e-03
## Ascobolus 1.630340e-03 4.107736e-03
## Trichosporiella 5.130137e-03 4.664717e-03
## Dendryphion 1.318764e-03 3.361779e-03
## Papiliotrema 7.231465e-03 1.112968e-02
## Incrupila 8.383572e-03 4.595095e-03
## Paraphoma 2.847661e-03 8.553639e-04
## Paraphaeosphaeria 6.753232e-03 5.778680e-03
## Didymella 1.521651e-03 1.790297e-04
## Haplosporangium 2.195525e-03 4.356388e-03
## X.uncultured.fungus. 1.793374e-02 4.230073e-02
## Burgoa 3.746160e-03 4.654771e-03
## Pseudoanguillospora 1.796997e-03 6.663882e-04
## Apiotrichum 7.173497e-04 2.456685e-03
## Exophiala 3.941800e-03 3.540809e-03
## NA. 4.463509e-03 5.868194e-03
## Aaosphaeria 5.731552e-03 4.406119e-03
## Pleotrichocladium 9.419744e-04 5.370890e-04
## Polyphilus 1.072402e-03 1.392453e-03
## Vishniacozyma 4.275114e-04 5.669273e-04
## Anungitopsis 6.086604e-04 5.967655e-04
## Cystobasidium 6.086604e-04 5.072507e-04
## Herpotrichia 5.217089e-04 3.471186e-03
## Dioszegia 9.709582e-04 1.889758e-03
## Boreoplaca 4.528723e-03 9.399057e-03
## Epicoccum 5.492435e-03 1.491914e-04
## Minimedusa 4.543215e-03 6.007440e-03
## Pseudeurotium 6.492377e-03 8.135903e-03
## Gorgomyces 1.014434e-03 1.790297e-04
## Neoascochyta 1.811489e-04 8.155796e-04
## Rhizophagus 1.202829e-03 2.924151e-03
## Umbelopsis 2.195525e-03 3.312049e-03
## Verrucula 5.144629e-04 5.470351e-04
## Cystofilobasidium 6.992348e-03 7.628653e-03
## Olpidium 6.347458e-03 6.166577e-04
## Barnettozyma 1.659324e-03 1.342722e-03
## Lambertella 6.376442e-04 1.064232e-03
## Yunzhangia 3.767898e-04 1.581429e-03
## Pseudogymnoascus 7.970552e-05 1.412345e-03
## Crocicreas 5.796765e-05 5.370890e-04
## Bullera 8.695148e-05 1.312884e-03
## Waitea 2.970842e-04 3.779515e-04
## Cladophialophora 2.608544e-04 5.171968e-04
## Cadophora 7.028578e-04 3.481132e-04
## Hamamotoa 3.294459e-05 1.989218e-04
## Stromatinia 2.753464e-04 1.094070e-04
## Galactomyces 1.304272e-04 1.292992e-04
## Itersonilia 7.970552e-04 1.680890e-03
## Knufia 7.390876e-04 7.161186e-04
## Cutaneotrichosporon 3.043302e-04 1.734525e-05
## Cladosporium 2.246247e-04 4.674663e-04
## Chromosera 9.419744e-05 4.774124e-04
## Rhodotorula 5.398169e-05 4.475741e-04
## Atractiella 1.811489e-04 2.088679e-04
## Funneliformis 7.390876e-04 6.763343e-04
## Alternaria 6.521361e-05 6.962265e-05
## Trichosporon 1.376732e-03 7.658491e-04
## Rhizoctonia 1.086894e-03 9.747170e-04
## Setophoma 9.419744e-05 2.074753e-05
## Monodictys 1.014434e-04 9.946092e-05
## Cantharellula 1.739030e-04 5.967655e-04
## Paraglomus 3.637470e-03 3.878976e-04
## Coprinellus 2.391166e-04 2.983828e-05
## Pseudosigmoidea 7.970552e-05 3.294459e-05
## Ganoderma 1.037377e-05 9.946092e-05
## Cryptotrichosporon 1.985861e-05 1.985861e-05
## Sporobolomyces 4.564953e-04 4.575202e-04
## Filobasidium 4.475741e-05 8.951483e-05
## Hyaloscypha 1.156350e-05 1.156350e-05
## Laetinaevia 8.038954e-05 8.038954e-05
## Paraboeremia 4.057736e-04 3.282210e-04
## Lachnellula 4.275114e-04 1.690836e-04
## Crassostrea 3.478059e-04 3.182749e-04
## Hannaella 3.732829e-05 1.392453e-04
## Pichia 1.159353e-04 1.690836e-04
## Fusarium 4.477345e-05 1.591375e-04
## Slooffia 1.666570e-04 1.094070e-04
## Degelia 1.492448e-05 1.492448e-05
## Varicosporium 3.622978e-05 8.951483e-05
## Aotearoamyces 2.173787e-05 1.392453e-04
## Boeremia 2.159268e-05 3.282210e-04
## Sampaiozyma 2.487414e-05 2.487414e-05
## Coniochaeta 1.883949e-04 1.989931e-05
## Claroideoglomus 4.149507e-05 3.580593e-04
## Entoloma 1.594110e-04 1.193531e-04
## Archaeospora 1.678923e-05 2.287601e-04
## Pulvinula 8.695148e-05 2.009754e-05
##
## $meta.dat.use
## ndvi_01
## 1 -1.9625407
## 2 0.9980715
## 3 -0.7571646
## 4 -1.1410253
## 5 -1.9896865
## 6 0.6228720
## 7 -0.8421145
## 8 0.9838132
## 9 0.6887884
## 10 0.5747102
## 11 0.5945529
## 12 -1.0529250
## 13 0.6121273
## 14 0.8302155
## 15 0.3130208
## 16 0.5095020
## 17 0.3558477
## 18 0.6619353
##
## $wald
## $wald$beta
## Mrakia Candida Sistotrema Bactrocera Mortierella
## (Intercept) 8.8942074 5.9501581 6.69553872 3.4486788 8.0724606
## ndvi_01 -0.3601436 0.1574983 -0.04584439 0.5230339 -0.1993473
## Sporidiobolus Juxtiphoma Tausonia Naganishia Saitozyma Botrytis
## (Intercept) 4.378760 5.7977898 5.2022449 5.1647632 5.59631589 4.3284318
## ndvi_01 1.422778 -0.1419552 -0.2499401 0.2131148 0.08000806 0.5395917
## Solicoccozyma Holtermanniella Tetracladium Ascobolus
## (Intercept) 6.0274208 4.112298297 4.3228940 2.7081407
## ndvi_01 -0.1421016 0.001351223 -0.9257339 -0.3980752
## Trichosporiella Dendryphion Papiliotrema Incrupila Paraphoma
## (Intercept) 3.8788327 2.2001977 3.90339220 3.2904963 1.1953614
## ndvi_01 -0.5978134 -0.6745859 -0.01556509 -0.5670846 -0.7174352
## Paraphaeosphaeria Didymella Haplosporangium X.uncultured.fungus.
## (Intercept) 3.0942738 0.8702958 2.512180538 4.55604227
## ndvi_01 -0.3118014 -0.4165883 -0.006015125 -0.05272315
## Burgoa Pseudoanguillospora Apiotrichum Exophiala NA.
## (Intercept) 1.0905213 0.7056184 0.7281833 2.1962112 2.9433932
## ndvi_01 0.4811883 -0.2889514 0.5792219 -0.2699503 -0.4221425
## Aaosphaeria Pleotrichocladium Polyphilus Vishniacozyma Anungitopsis
## (Intercept) 1.627750 0.7803799 0.5066444 0.09994638 -0.03021235
## ndvi_01 -0.154652 -0.4027885 -0.0336807 0.70313913 -0.29722369
## Cystobasidium Herpotrichia Dioszegia Boreoplaca Epicoccum
## (Intercept) -0.7541884 1.3196872 0.1182307 0.6216776 0.7130020
## ndvi_01 -0.1413814 0.1869529 1.2564446 1.4692198 0.8812529
## Minimedusa Pseudeurotium Gorgomyces Neoascochyta Rhizophagus
## (Intercept) 2.2393766 1.0811724 -0.7467174 -0.9198901 0.6303061
## ndvi_01 0.1106339 0.2351272 -1.1444276 0.3902196 0.1424985
## Umbelopsis Verrucula Cystofilobasidium Olpidium Barnettozyma
## (Intercept) 0.7313788 -0.9318895 2.087582 0.5362518 -0.2739397
## ndvi_01 -0.1120565 0.2656547 1.834111 -1.3704642 0.2198452
## Lambertella Yunzhangia Pseudogymnoascus Crocicreas Bullera
## (Intercept) 0.4675277 -1.050468 -1.5272946 -1.9350892 -0.4190255
## ndvi_01 -0.5461814 0.200010 -0.2757864 -0.3654408 1.2613358
## Waitea Cladophialophora Cadophora Hamamotoa Stromatinia
## (Intercept) -2.310343 -1.3192438 -0.4508063 -1.8344779 -2.1378947
## ndvi_01 -0.150909 -0.1921385 -1.0389346 -0.1222196 0.3263724
## Galactomyces Itersonilia Knufia Cutaneotrichosporon
## (Intercept) -2.3244010 -1.006609 -1.3062605 -2.2859292
## ndvi_01 0.2685759 1.470499 -0.1434756 -0.1193485
## Cladosporium Chromosera Rhodotorula Atractiella Funneliformis
## (Intercept) -1.8088405 -3.1606752 -1.430221 -2.4881863 -2.0389944
## ndvi_01 0.6806295 -0.6858619 1.013241 -0.1973964 0.8488935
## Alternaria Trichosporon Rhizoctonia Setophoma Monodictys
## (Intercept) -2.6384939 -0.2586130 -1.7380132 -3.205879 -3.089877
## ndvi_01 -0.4832859 0.4016763 0.5911897 -1.352938 -0.786227
## Cantharellula Paraglomus Coprinellus Pseudosigmoidea Ganoderma
## (Intercept) -1.8537510 -2.611189 -2.947229 -2.8262741 -4.1514081
## ndvi_01 -0.8554298 1.254794 -1.152908 -0.2613889 -0.2648695
## Cryptotrichosporon Sporobolomyces Filobasidium Hyaloscypha
## (Intercept) -3.047558 -0.7202827 -2.1413678 -3.582271
## ndvi_01 -1.395587 0.7124375 0.1896753 -1.854668
## Laetinaevia Paraboeremia Lachnellula Crassostrea Hannaella
## (Intercept) -1.729770 -1.9753666 -3.29950881 -2.6838667 -3.0090247
## ndvi_01 -0.107517 -0.1434928 0.06419128 0.7530111 -0.3863337
## Pichia Fusarium Slooffia Degelia Varicosporium
## (Intercept) -2.160248 -2.5703379 -3.9492510 -4.077829 -3.92117996
## ndvi_01 0.390980 0.2070224 0.7868061 -1.069650 -0.05247627
## Aotearoamyces Boeremia Sampaiozyma Coniochaeta Claroideoglomus
## (Intercept) -4.8110111 -2.9238876 -3.1487158 -3.6295732 -2.9362944
## ndvi_01 0.4519739 0.8893452 0.2415206 -0.3330647 0.4324119
## Entoloma Archaeospora Pulvinula
## (Intercept) -3.4172896 -3.9305752 -3.9484818
## ndvi_01 -0.3168533 0.1563548 -0.1709763
##
## $wald$sig
## Mrakia Candida Sistotrema
## 0.6233013 1.3671077 1.2090721
## Bactrocera Mortierella Sporidiobolus
## 1.7850198 0.4267081 1.3338179
## Juxtiphoma Tausonia Naganishia
## 0.5763258 0.8396484 0.7488569
## Saitozyma Botrytis Solicoccozyma
## 0.3070906 1.2906217 0.2856745
## Holtermanniella Tetracladium Ascobolus
## 0.7197022 0.8221235 0.9145743
## Trichosporiella Dendryphion Papiliotrema
## 0.7645297 0.8300203 0.6479855
## Incrupila Paraphoma Paraphaeosphaeria
## 0.5683365 1.9143931 0.6643021
## Didymella Haplosporangium X.uncultured.fungus.
## 1.4781773 0.6210499 0.5899659
## Burgoa Pseudoanguillospora Apiotrichum
## 1.3618695 1.3066608 1.3714338
## Exophiala NA. Aaosphaeria
## 0.4636157 0.7016399 0.9777038
## Pleotrichocladium Polyphilus Vishniacozyma
## 1.0041378 0.7986170 1.0678246
## Anungitopsis Cystobasidium Herpotrichia
## 0.5618864 1.4661328 2.3161234
## Dioszegia Boreoplaca Epicoccum
## 2.0625747 1.3008227 1.6801985
## Minimedusa Pseudeurotium Gorgomyces
## 1.0618424 1.3603130 1.4640745
## Neoascochyta Rhizophagus Umbelopsis
## 1.5293936 1.2550695 1.0927633
## Verrucula Cystofilobasidium Olpidium
## 0.4742074 2.2826783 2.3013192
## Barnettozyma Lambertella Yunzhangia
## 2.0963256 0.8273172 1.4488712
## Pseudogymnoascus Crocicreas Bullera
## 1.3857237 1.9252008 1.8716225
## Waitea Cladophialophora Cadophora
## 1.8664913 1.6806481 0.8417828
## Hamamotoa Stromatinia Galactomyces
## 1.8643526 1.4471203 1.6001287
## Itersonilia Knufia Cutaneotrichosporon
## 1.4662815 1.6166129 2.1768257
## Cladosporium Chromosera Rhodotorula
## 1.3818130 1.5339243 1.7041205
## Atractiella Funneliformis Alternaria
## 1.8825444 1.7020546 1.2992293
## Trichosporon Rhizoctonia Setophoma
## 1.5709749 2.5254568 1.5117499
## Monodictys Cantharellula Paraglomus
## 1.5440277 1.2742531 2.7103630
## Coprinellus Pseudosigmoidea Ganoderma
## 1.8704190 1.3395788 1.4815535
## Cryptotrichosporon Sporobolomyces Filobasidium
## 1.7196435 1.3416815 1.8317296
## Hyaloscypha Laetinaevia Paraboeremia
## 1.5239400 1.2407940 1.2661065
## Lachnellula Crassostrea Hannaella
## 2.1975052 1.3496368 0.8585331
## Pichia Fusarium Slooffia
## 1.3334910 1.4121643 1.6219349
## Degelia Varicosporium Aotearoamyces
## 1.1695741 1.2568640 1.1955767
## Boeremia Sampaiozyma Coniochaeta
## 2.3008104 1.9711127 1.6788731
## Claroideoglomus Entoloma Archaeospora
## 1.0483549 1.0843439 1.6011218
## Pulvinula
## 1.1815147
##
## $wald$X
## (Intercept) ndvi_01
## 1 1 -1.9625407
## 2 1 0.9980715
## 3 1 -0.7571646
## 4 1 -1.1410253
## 5 1 -1.9896865
## 6 1 0.6228720
## 7 1 -0.8421145
## 8 1 0.9838132
## 9 1 0.6887884
## 10 1 0.5747102
## 11 1 0.5945529
## 12 1 -1.0529250
## 13 1 0.6121273
## 14 1 0.8302155
## 15 1 0.3130208
## 16 1 0.5095020
## 17 1 0.3558477
## 18 1 0.6619353
## attr(,"assign")
## [1] 0 1
##
## $wald$bias
## [1] -2.2308186 -0.1560564
# Show effect size and significance plots
linda.plot(
l_model,
variables.plot = c('ndvi_01'),
alpha = 0.05,
lfc.cut = 1,
legend = TRUE
)
## $plot.lfc
## $plot.lfc[[1]]
##
##
## $plot.volcano
## $plot.volcano[[1]]
l_model_df <- as.data.frame(l_model$output)
write.xlsx(l_model_df, file = "supplementary_table_ndvi_regression_fits1.xlsx", rowNames = TRUE, colnames = TRUE)
This script is based on ideas and code from the dada2 Tutorial by Benjamin Callahan, the publication “Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses” by Callahan et al. (2016) and various pages of the official phyloseq website by Paul J. McMurdie.
sessionInfo()
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Oslo
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] MicrobiomeStat_1.2 aod_1.3.3 BBmisc_1.13
## [4] betareg_3.2-1 ggpmisc_0.6.1 ggpp_0.5.8-1
## [7] openxlsx_4.2.7.1 readxl_1.4.3 lubridate_1.9.4
## [10] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
## [13] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
## [16] tibble_3.2.1 tidyverse_2.0.0 ggpubr_0.6.0
## [19] viridis_0.6.5 viridisLite_0.4.2 phangorn_2.12.1
## [22] biomformat_1.34.0 vegan_2.6-8 lattice_0.22-6
## [25] permute_0.9-7 scales_1.3.0 gridExtra_2.3
## [28] ape_5.8-1 reshape2_1.4.4 phyloseq_1.50.0
## [31] ggplot2_3.5.1 cuphyr_0.3 DECIPHER_3.2.0
## [34] Biostrings_2.74.0 GenomeInfoDb_1.42.1 XVector_0.46.0
## [37] IRanges_2.40.0 S4Vectors_0.44.0 BiocGenerics_0.52.0
## [40] dada2_1.34.0 Rcpp_1.0.13-1
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.5 matrixStats_1.4.1
## [3] bitops_1.0-9 devtools_2.4.5
## [5] httr_1.4.7 RColorBrewer_1.1-3
## [7] numDeriv_2016.8-1.1 profvis_0.4.0
## [9] tools_4.4.2 backports_1.5.0
## [11] utf8_1.2.4 R6_2.5.1
## [13] mgcv_1.9-1 rhdf5filters_1.18.0
## [15] urlchecker_1.0.1 withr_3.0.2
## [17] quantreg_5.99.1 cli_3.6.3
## [19] Biobase_2.66.0 textshaping_0.4.1
## [21] formatR_1.14 sandwich_3.1-1
## [23] labeling_0.4.3 sass_0.4.9
## [25] Rsamtools_2.22.0 systemfonts_1.1.0
## [27] sessioninfo_1.2.2 rstudioapi_0.17.1
## [29] generics_0.1.3 hwriter_1.3.2.1
## [31] car_3.1-3 zip_2.3.1
## [33] Matrix_1.7-1 interp_1.1-6
## [35] abind_1.4-8 lifecycle_1.0.4
## [37] yaml_2.3.10 carData_3.0-5
## [39] SummarizedExperiment_1.36.0 rhdf5_2.50.0
## [41] SparseArray_1.6.0 grid_4.4.2
## [43] promises_1.3.2 crayon_1.5.3
## [45] pwalign_1.2.0 miniUI_0.1.1.1
## [47] cowplot_1.1.3 pillar_1.10.0
## [49] knitr_1.49 GenomicRanges_1.58.0
## [51] statip_0.2.3 boot_1.3-31
## [53] codetools_0.2-20 fastmatch_1.1-4
## [55] glue_1.8.0 ShortRead_1.64.0
## [57] data.table_1.16.4 remotes_2.5.0
## [59] vctrs_0.6.5 png_0.1-8
## [61] cellranger_1.1.0 gtable_0.3.6
## [63] cachem_1.1.0 xfun_0.49
## [65] S4Arrays_1.6.0 mime_0.12
## [67] modeest_2.4.0 survival_3.8-3
## [69] timeDate_4041.110 iterators_1.0.14
## [71] statmod_1.5.0 ellipsis_0.3.2
## [73] nlme_3.1-166 usethis_3.1.0
## [75] fBasics_4041.97 bslib_0.8.0
## [77] rpart_4.1.23 colorspace_2.1-1
## [79] DBI_1.2.3 nnet_7.3-19
## [81] ade4_1.7-22 tidyselect_1.2.1
## [83] timeSeries_4041.111 compiler_4.4.2
## [85] curl_6.0.1 SparseM_1.84-2
## [87] DelayedArray_0.32.0 checkmate_2.3.2
## [89] lmtest_0.9-40 quadprog_1.5-8
## [91] spatial_7.3-17 digest_0.6.37
## [93] minqa_1.2.8 rmarkdown_2.29
## [95] htmltools_0.5.8.1 pkgconfig_2.0.3
## [97] jpeg_0.1-10 lme4_1.1-35.5
## [99] MatrixGenerics_1.18.0 stabledist_0.7-2
## [101] fastmap_1.2.0 rlang_1.1.4
## [103] htmlwidgets_1.6.4 UCSC.utils_1.2.0
## [105] shiny_1.10.0 farver_2.1.2
## [107] jquerylib_0.1.4 zoo_1.8-12
## [109] jsonlite_1.8.9 BiocParallel_1.40.0
## [111] magrittr_2.0.3 polynom_1.4-1
## [113] modeltools_0.2-23 Formula_1.2-5
## [115] GenomeInfoDbData_1.2.13 Rhdf5lib_1.28.0
## [117] munsell_0.5.1 stringi_1.8.4
## [119] stable_1.1.6 zlibbioc_1.52.0
## [121] MASS_7.3-61 plyr_1.8.9
## [123] pkgbuild_1.4.5 flexmix_2.3-19
## [125] ggrepel_0.9.6 deldir_2.0-4
## [127] splines_4.4.2 multtest_2.62.0
## [129] hms_1.1.3 igraph_2.1.2
## [131] ggsignif_0.6.4 rmutil_1.1.10
## [133] pkgload_1.4.0 evaluate_1.0.1
## [135] latticeExtra_0.6-30 RcppParallel_5.1.9
## [137] nloptr_2.1.1 tzdb_0.4.0
## [139] foreach_1.5.2 httpuv_1.6.15
## [141] MatrixModels_0.5-3 clue_0.3-66
## [143] broom_1.0.7 xtable_1.8-4
## [145] rstatix_0.7.2 later_1.4.1
## [147] ragg_1.3.3 lmerTest_3.1-3
## [149] memoise_2.0.1 GenomicAlignments_1.42.0
## [151] cluster_2.1.8 timechange_0.3.0