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 = "Nems_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)
library(cowplot)
# 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-Nem
## 10-Nem
## 11-Nem
## 12-Nem
## 13-Nem
## 14-Nem
## 15-Nem
## 16-Nem
## 17-Nem
## 18-Nem
## 2-Nem
## 3-Nem
## 4-Nem
## 5-Nem
## 6-Nem
## 7-Nem
## 8-Nem
## 9-Nem
## NegativK-4-Nem
## Positivkontroll-Nem
##
## The number of samples in the file is:
## 20
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("NegativK-4-Nem", "Vann", "Neg", "Positivkontroll-Nem", "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 = "Nems_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:
##
## 2
##
##
## The following controls were pruned:
##
## NegativK-4-Nem
## Positivkontroll-Nem
## 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")
}
}
# Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
ps <- prune_taxa(taxa_sums(ps) > 0, ps)
sample_data(ps)[["ndvi"]] <- as.numeric(sample_data(ps)[["ndvi"]])
# Transformed per sample (per-sample relative abundance)
ps.trans <- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
# Read descriptor values as numeric
sample_data(ps.trans)[["ndvi"]] <- as.numeric(sample_data(ps.trans)[["ndvi"]])
sample_data(ps.trans)[["ndvi_july"]] <- as.numeric(sample_data(ps.trans)[["ndvi_july"]])
# 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, ndvi_july, 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$biom_export == "TRUE") {
suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
"all_samples.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
"samples_without_controls.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
"samples_without_controls_rel_abundance.biom"), biom_format = "standard"))
suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
"just_controls.biom"), biom_format = "standard"))
}
ps
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 151 taxa and 18 samples ]
## sample_data() Sample Data: [ 18 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 151 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 151 reference sequences ]
The chunks below will produce various plots and other output. Each chunk is headed by a description of the output and may contain some parameters to adjust the output.
This chunk sets the background structure and color palette. Viridis was chosen because it is optimized for grey-scale printing and various types of color blindness and More info on the Viridis palette can be found on the Viridis info page. It also establishes save_plot as a shorter variant of ggsave with customized date-time structure to save plots with the same name mulitple times instead of overwriting them (overwriting can be triggered with overwrite=TRUE).
##### Optional settings (sensible defaults) #####
# Can be changed to adjust the output format for all plots. Default "pdf",
# possible "eps"/"ps", "tex" (pictex), "jpeg", "tiff", "png", "bmp" and "svg"
parameters$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$color_by = "Symptoms"
##### Optional settings (sensible defaults) #####
# Can be changed to change the width (in cm) of the saved plot.
set$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$overview_ps, nrow = 2, align = "v",
common.legend = TRUE, legend = "right")
if (length(tmp$noise_ASVs) > 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 = "Sample"
# 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$label_shape_by = "Symptoms"
##### Optional settings (sensible defaults) #####
# Can be changed to generate a tree for just the control sequences IF no
# phylogenetic tree for all seuquences is provided. This may slow down this
# chunk when running it for the first time
set$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 = 15,
output_format = "tops",
ignore_na = TRUE,
silent = FALSE)
##
## The top 15 most abundant annotated groups at the taxonomic level 'Genus' are:
## Globodera
## Meloidogyne
## Pristionchus
## Rhabditis
## Pellioditis
## Cephaloboides
## Aporcelaimellus
## Acrobeloides
## Nygolaimus
## Cruznema
## Sectonema
## Acrobeles
## Eucephalobus
## Mesorhabditis
## Aphelenchoides
This chunk gives an overview of the number of ASVs belonging to which Phylum
# Number of ASVs belonging to Nematoda
ps_tbl %>% select(OTU, Phylum) %>% unique() %>% group_by(Phylum) %>% summarise(length(OTU))
## # A tibble: 5 × 2
## Phylum `length(OTU)`
## <chr> <int>
## 1 Arthropoda 10
## 2 Candidatus Bipolaricaulota 1
## 3 Nematoda 133
## 4 Proteobacteria 1
## 5 Tardigrada 6
# Total ASV counts
ps_tbl %>% select(OTU, Abundance) %>% summarise(total_sum = sum(Abundance))
## # A tibble: 1 × 1
## total_sum
## <int>
## 1 405276
# Total ASV counts per phylum
ps_tbl %>% select(OTU, Phylum, Abundance) %>% group_by(Phylum) %>% summarise(Abundance_sum = sum(Abundance))
## # A tibble: 5 × 2
## Phylum Abundance_sum
## <chr> <int>
## 1 Arthropoda 426
## 2 Candidatus Bipolaricaulota 4
## 3 Nematoda 404660
## 4 Proteobacteria 11
## 5 Tardigrada 175
# Percentage of ASV counts belonging to Nematoda
nem_percentage = 404660/405276 # ASV counts Nematoda / Total ASV counts
nem_percentage
## [1] 0.99848
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 sample category that will be shown in separate panels.
# Accepted values are the column headers in your descriptor file.
set$panel_by = "Symptoms"
# CHANGE ME to the desired sample categories on the x-axis. Accepted values
# are the column headers in the descriptor file.
set$x_axis_value = "Alias"
# CHANGE ME to the count of top ASVs you want to plot (e.g. 'set$topASVs = 20'
# plots the 20 most abundant ASVs)
set$topASVs = 100
# 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 = 15
# CHANGE ME to an entry at the chosen taxonomic level you want to highlight.
# Comment out to not highlight anything.
set$highlight = "Ditylenchus"
##### Optional settings (sensible defaults) #####
# Can be changed to turn unified coloring on or off (same taxonomy term = same
# color in both plots). Highlighting will unify colors even if unify_colors is
# FALSE.
set$unify_colors = TRUE
# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
set$ignore_na = FALSE
# Can be changed to remove ASV segmentation in the top n taxlvl plot. This
# improves visual clarity when a bar segment appears black due to the border of
# many small ASVs overlapping.
set$fuse_ASVs = FALSE
# Can be changed to change the width (in cm) of the saved plot.
set$wp = 30
# Can be changed to change the height (in cm) of the saved plot.
set$hp = 20
# Can be changed to change the resolution (in dpi) of the saved plot.
set$res = 300
# Can be changed to change the y-axis label
set$y_axis_label = "Relative abundance"
############### NO NEED FOR CHANGES BELOW ###############
# 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)
set$topnASVs <- names(sort(taxa_sums(ps), decreasing = TRUE))[1:set$topASVs]
set$ps.topnASVs <- prune_taxa(set$topnASVs, ps.trans)
if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
set$toptax <- union(phyloseq::tax_table(set$ps.topnTax)[, set$taxlvl], phyloseq::tax_table(set$ps.topnASVs)[,
set$taxlvl])
set$toptax <- sort(set$toptax)
set$taxlvlPalette <- viridis(length(set$toptax))
names(set$taxlvlPalette) <- set$toptax
if (exists("highlight", envir = set)) {
# It is possible to change the highlight color here by substituting
# 'sub_viridis$reds[4]' with a hexcode-string, e.g. '#ff7f7f''
set$taxlvlPalette[set$highlight] <- sub_viridis$reds[4]
}
set$taxlvlPalette <- set$taxlvlPalette[sort(names(set$taxlvlPalette))]
set$my_scale_fill <- scale_fill_manual(values = set$taxlvlPalette, na.value = "grey")
} else {
set$my_scale_fill <- my_scale_fill
}
# Plot
if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
set$my_scale_fill <- scale_fill_manual(values = set$taxlvlPalette[sort(unique(phyloseq::tax_table(set$ps.topnTax)[,
set$taxlvl]))], na.value = "grey")
}
plots$topn_tax <- plot_bar(set$ps.topnTax, x = set$x_axis_value, fill = set$taxlvl,
title = paste0("Top ", set$top_n, " ", set$taxlvl)) + facet_grid(paste0("~",
set$panel_by), scales = "free", space = "free") + set$my_scale_fill + ylab(set$y_axis_label) +
xlab("Sample") + theme(strip.background = element_blank(), strip.text = element_text(size = 16),
axis.title = element_text(size = 16), legend.text = element_text(size = 14))
if (set$fuse_ASVs) {
plots$topn_tax <- plots$topn_tax + geom_bar(aes_string(color = set$taxlvl, fill = set$taxlvl),
stat = "identity", position = "stack") + scale_color_manual(values = set$taxlvlPalette,
na.value = NA)
}
if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
set$my_scale_fill <- scale_fill_manual(values = set$taxlvlPalette[sort(unique(phyloseq::tax_table(set$ps.topnASVs)[,
set$taxlvl]))], na.value = "grey")
}
plots$topn_ASVs <- plot_bar(set$ps.topnASVs, x = set$x_axis_value, fill = set$taxlvl,
title = paste0("Top", set$topASVs, "_ASVs")) + facet_grid(paste0("~", set$panel_by),
scales = "free_x", space = "free") + set$my_scale_fill + ylab(set$y_axis_label) +
xlab("Sample") + theme(strip.background = element_blank())
# save
save_plot(plots$topn_tax, plot_name = paste0("Top", set$top_n, "_", set$taxlvl),
filetype = tmp$out)
save_plot(plots$topn_ASVs, plot_name = paste0("Top", set$topASVs, "_ASVs"), filetype = tmp$out)
# Clean up plot parameters
rm(list = ls(set), envir = set)
# Print to standard out
plots$topn_tax
plots$topn_ASVs
This chunk plots abundance of the Top n ASVs or taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n may lead to unreadable plots. The chunk does not require any input, but it is possible to adjust the default ‘n’, and to change width, height and resolution of the PDF-output if necessary.
# CHANGE ME to the desired sample categories on the x-axis.
# Accepted values are the column headers in the descriptor file.
set$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 =
##### 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")
# 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, ndvi_july, Abundance, Shannon) %>%
filter(Abundance > 0) %>%
group_by(Genus, Alias, ndvi, ndvi_july, 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$x_axis_label) +
theme(strip.background = element_blank(),
#strip.text = element_text(size = 12),
#axis.title=element_text(size=12),
#legend.text = element_text(size=12),
legend.position = "bottom")
plots$ndvi_dot_plot <- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
y = ndvi)) +
geom_point() +
theme(strip.background = element_blank(),
# strip.text = element_text(size = 12),
# axis.title=element_text(size=12),
# legend.text = element_text(size=12),
axis.title.x=element_blank()) +
ylab("NDVI")
plots$shannon_dot_plot <- ggplot(set$topntax_tbl,
aes(fct_reorder(Alias, ndvi),
y = Shannon)) +
geom_point() +
theme(strip.background = element_blank(),
# strip.text = element_text(size = 12),
# axis.title=element_text(size=12),
# legend.text = element_text(size=12),
axis.title.x=element_blank()) +
ylab("Shannon")
plots$combo_topn_custom <- ggarrange(plots$ndvi_dot_plot,
plots$shannon_dot_plot,
plots$topn_tax_custom,
nrow = 3,
heights = c(1, 1, 3),
align = "v")
plots$combo_topn_custom
save_plot(plots$combo_topn_custom, plot_name = paste0("Customized_NDVI_Shannon_plot"),
filetype = tmp$out)
set$my_formula <- y ~ x
set$plot_title <- "Nematodes"
topntax_data <- set$topntax_tbl %>%
mutate(Taxa = 'nematodes') %>%
ungroup() %>%
select(Alias, ndvi, Shannon, Taxa) %>%
distinct()
write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_nematodes.csv")
# Excel file with field data
morphological_id <- file.path("2020-patch-larvik-a-counts.csv")
# Reduce abundance per sample and genus data to genera of interest
genera_of_interest_mol_morph <- c("Globodera", "Meloidogyne", "Pratylenchus")
genus_abundance_tbl_per_sample_mm <- genus_abundance_tbl_per_sample %>%
filter(Genus %in% genera_of_interest_mol_morph)
# read in and parse morphological count data
morph_data <- read_delim(morphological_id) %>%
mutate(genus = str_replace(genus, "cyst", "Globodera"),
genus = str_replace(genus, "mel", "Meloidogyne"),
genus = str_replace(genus, "prat", "Pratylenchus")) %>%
dplyr::rename(Sample = rute, Genus = genus)
# Combine morphological and metabarcoding data
set$data_mol_morph <- genus_abundance_tbl_per_sample_mm %>%
left_join(morph_data)
### Plotting
# New facet label names for Genus variable
genus_names <- list(
'Globodera'=expression(paste(italic("Globodera "))), #"spp.")),
'Meloidogyne'=expression(paste(italic("Meloidogyne "))), #"spp.")),
'Pratylenchus'=expression(paste(italic("Pratylenchus ")))#, "spp."))
)
genus_labeller <- function(variable,value){
return(genus_names[value])
}
signif.labs <- c("pval > 0.05", "pval <= 0.05")
names(signif.labs) <- c("p value > 0.05", "p value <= 0.05")
# Subset data for modeling
set$glob_data <- subset(set$data_mol_morph, Genus == "Globodera")
set$mel_data <- subset(set$data_mol_morph, Genus == "Meloidogyne")
set$prat_data <- subset(set$data_mol_morph, Genus == "Pratylenchus")
# Quasi-binomial model for Globodera
model_glob = glm(
Genus_Sample_Abundance ~ total_count,
data = set$glob_data,
family = quasibinomial
)
# Quasi-binomial model for Meloidogyne
model_mel = glm(
Genus_Sample_Abundance ~ total_count,
data = set$mel_data,
family = quasibinomial
)
# Quasi-binomial model for Pratylenchus
model_prat = glm(
Genus_Sample_Abundance ~ total_count,
data = set$prat_data,
family = quasibinomial
)
pval_glm <- function(glm){
coef(summary(glm))[,4][2]
}
set$data_mol_morph <- set$data_mol_morph %>%
mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob), NA)) %>%
mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat), pval)) %>%
mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel), pval)) %>%
mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
# Other plotting variables
# Can be changed to change the width (in cm) of the saved plot.
set$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
set$my_formula <- y ~ x
set$plot_title <- paste("Molecular analysis vs",
"morpological analysis", sep = "\n")
# Plotting quasibinomial model
plots$mol_morph_quasi <- ggplot(set$data_mol_morph,
aes(x=total_count,
y = Genus_Sample_Abundance)) +
geom_point(alpha = 0.7) +
#ggtitle(set$plot_title) +
theme(plot.title = element_text(hjust = 0.5, size = 20),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
#axis.line = element_line(colour = "dark grey"),
strip.background = element_blank(),
strip.text = element_text(face = "italic", size = 16),
axis.text = element_text(),
text = element_text()) +
xlab("Nematodes / 250 ml soil") +
ylab("Relative abundance") +
geom_smooth(method = "glm", formula = set$my_formula, se = F,
method.args = list(family = quasibinomial),
aes(color = signif, linetype = signif)) +
scale_color_manual(values=c('black', 'lightgrey'),
name= 'Significance',
labels = names(signif.labs)) +
scale_linetype_discrete(name= 'Significance',
labels = names(signif.labs)) +
facet_wrap(~Genus, scales = "free",
#labeller = labeller(Genus = genus_labs)) +
labeller = genus_labeller)
#Print and save plot
plots$mol_morph_quasi
save_plot(plots$mol_morph_quasi, plot_name = paste0("Molecular_and_Morphological_Plot"),
filetype = ".pdf")
# Generate table overview of morphological counts for manuscript
morph_table_manuscript <- pivot_wider(set$data_mol_morph %>%
select(-Genus_Sample_Abundance,
-ndvi),
names_from = Genus,
values_from = total_count) %>%
arrange(Alias)
write.xlsx(morph_table_manuscript, file = "t1_morph_table_manuscript.xlsx")
# Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
# containing the sample abundances for that genus.
ldf <- data.frame(genus_abundance_tbl_per_sample %>% pivot_wider(id_cols = c('Sample', 'ndvi'), names_from = 'Genus', values_from = 'Genus_Sample_Abundance'))
ldf_genus_data <- data.frame(ldf) %>% select(!c('Sample', 'ndvi'))
colnames(ldf_genus_data) <- gsub(' ', '.', colnames(ldf_genus_data))
ldf <- cbind.data.frame(
Sample = ldf$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 genera 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 53 features will be tested!
## Fit linear models ...
## Completed.
# Print model info
l_model
## $variables
## [1] "ndvi_01"
##
## $bias
## [1] -0.01789055
##
## $output
## $output$ndvi_01
## baseMean log2FoldChange lfcSE stat
## Globodera 754634.19865 -4.559618e-01 0.29935505 -1.523147257
## Meloidogyne 61866.79371 2.765057e+00 0.40120028 6.891961655
## Pristionchus 67346.31537 3.169910e-01 0.33017787 0.960061242
## Rhabditis 56521.12829 4.432402e-01 0.27148299 1.632662775
## Nygolaimus 3165.10477 -8.427746e-01 0.36100641 -2.334514200
## Acrobeloides 4388.62890 7.941938e-01 0.24592277 3.229443800
## Aporcelaimellus 8752.47207 -3.406609e-01 0.16569033 -2.056009379
## Pellioditis 16832.59394 -2.604002e-01 0.43525867 -0.598265365
## Cephaloboides 2025.06777 5.911949e-01 0.59646282 0.991168138
## Eucephalobus 646.22070 -4.151353e-01 0.45558400 -0.911215723
## Acrobeles 651.64733 5.235050e-01 0.59599188 0.878375980
## Mesorhabditis 524.38406 6.847116e-01 0.42269495 1.619871742
## Pratylenchus 392.39406 6.840229e-01 0.35193863 1.943585597
## Hypsibius 242.94694 -2.893259e-01 0.35082408 -0.824703697
## Aphelenchus 316.56727 2.529986e-02 0.48480642 0.052185488
## Cruznema 1363.79265 8.024877e-01 0.49336009 1.626575985
## Aphelenchoides 822.11300 -2.162205e-01 0.38616715 -0.559914237
## Anaplectus 458.84418 -5.849614e-05 0.41156785 -0.000142130
## Metateratocephalus 536.81600 -7.507380e-01 0.33206049 -2.260847143
## Plectus 434.97545 -6.856073e-01 0.42590207 -1.609776919
## Paramphidelus 498.11205 -3.340229e-01 0.36976266 -0.903343935
## Filenchus 259.63928 -3.615399e-01 0.34704143 -1.041777350
## Ditylenchus 425.99207 -5.460422e-01 0.29454627 -1.853841900
## Sectonema 233.31224 -8.387025e-01 0.47130542 -1.779530807
## Chiloplacus 758.90167 2.180144e-01 0.18503697 1.178220939
## Bastiania 98.23107 -3.367691e-01 0.28834536 -1.167936648
## Clarkus 119.63544 -5.583309e-01 0.23586714 -2.367141657
## Trischistoma 275.44460 -8.092726e-02 0.37115882 -0.218039431
## Tylencholaimus 353.94743 -6.167086e-02 0.25399021 -0.242808008
## Zeldia 800.30996 6.173047e-02 0.28823133 0.214169867
## Zeatylenchus 214.31405 6.287060e-02 0.31691865 0.198380869
## Prismatolaimus 355.13517 2.221236e-01 0.15762989 1.409146273
## Diploscapter 691.90325 3.177631e-01 0.25758203 1.233638597
## Prodesmodora 199.12220 1.613313e-01 0.17248893 0.935314184
## Panagrolaimus 411.24634 7.120651e-02 0.10241779 0.695255342
## Diphascon 142.12094 1.333432e-02 0.11281091 0.118200620
## Mesaphorura 211.59911 7.592354e-02 0.08824782 0.860344677
## Tullbergia 64.19880 -1.033518e-01 0.16089301 -0.642363814
## Spelobia 6776.13632 2.047921e-02 0.08477302 0.241577026
## Bunonema 1135.86360 -8.197451e-02 0.10298133 -0.796013368
## Aquatides 950.11625 -1.370781e-01 0.11024072 -1.243443740
## Myolaimus 607.64266 2.047921e-02 0.08477302 0.241577026
## Altainella 526.06316 8.944697e-04 0.12485257 0.007164207
## Dicyrtomina 393.04080 -6.957390e-02 0.11101082 -0.626730773
## Anguina 262.10103 -1.370781e-01 0.11024072 -1.243443740
## Deladenus 222.46410 2.879846e-02 0.10715623 0.268752044
## Geobacter 221.88520 -1.354813e-01 0.12565904 -1.078166078
## NA. 221.38501 1.493595e-02 0.12460371 0.119867621
## Odontocepheus 217.59550 1.596974e-02 0.13517441 0.118141742
## Theristus 158.90293 2.879846e-02 0.10715623 0.268752044
## Thonus 116.90292 8.944697e-04 0.12485257 0.007164207
## Candidatus.Acetothermum 116.90292 8.944697e-04 0.12485257 0.007164207
## Sellnickochthonius 36.82683 2.047921e-02 0.08477302 0.241577026
## pvalue padj reject df
## Globodera 1.472409e-01 0.5574120890 FALSE 16
## Meloidogyne 3.623613e-06 0.0001920515 TRUE 16
## Pristionchus 3.513092e-01 0.7352513393 FALSE 16
## Rhabditis 1.220616e-01 0.5177548997 FALSE 16
## Nygolaimus 3.292834e-02 0.4034160983 FALSE 16
## Acrobeloides 5.242908e-03 0.1389370590 FALSE 16
## Aporcelaimellus 5.647602e-02 0.4988715484 FALSE 16
## Pellioditis 5.580376e-01 0.8450283790 FALSE 16
## Cephaloboides 3.363572e-01 0.7352513393 FALSE 16
## Eucephalobus 3.757063e-01 0.7352513393 FALSE 16
## Acrobeles 3.927397e-01 0.7352513393 FALSE 16
## Mesorhabditis 1.247991e-01 0.5177548997 FALSE 16
## Pratylenchus 6.974921e-02 0.5177548997 FALSE 16
## Hypsibius 4.216652e-01 0.7449418642 FALSE 16
## Aphelenchus 9.590269e-01 0.9998883537 FALSE 16
## Cruznema 1.233578e-01 0.5177548997 FALSE 16
## Aphelenchoides 5.832927e-01 0.8587364852 FALSE 16
## Anaplectus 9.998884e-01 0.9998883537 FALSE 16
## Metateratocephalus 3.805812e-02 0.4034160983 FALSE 16
## Plectus 1.269965e-01 0.5177548997 FALSE 16
## Paramphidelus 3.797431e-01 0.7352513393 FALSE 16
## Filenchus 3.130015e-01 0.7352513393 FALSE 16
## Ditylenchus 8.229094e-02 0.5177548997 FALSE 16
## Sectonema 9.415207e-02 0.5177548997 FALSE 16
## Chiloplacus 2.559357e-01 0.6888466043 FALSE 16
## Bastiania 2.599421e-01 0.6888466043 FALSE 16
## Clarkus 3.086977e-02 0.4034160983 FALSE 16
## Trischistoma 8.301549e-01 0.9955117434 FALSE 16
## Tylencholaimus 8.112394e-01 0.9955117434 FALSE 16
## Zeldia 8.331202e-01 0.9955117434 FALSE 16
## Zeatylenchus 8.452458e-01 0.9955117434 FALSE 16
## Prismatolaimus 1.779308e-01 0.6286888072 FALSE 16
## Diploscapter 2.351534e-01 0.6888466043 FALSE 16
## Prodesmodora 3.635293e-01 0.7352513393 FALSE 16
## Panagrolaimus 4.968695e-01 0.8229401169 FALSE 16
## Diphascon 9.073799e-01 0.9998883537 FALSE 16
## Mesaphorura 4.023073e-01 0.7352513393 FALSE 16
## Tullbergia 5.297345e-01 0.8412595983 FALSE 16
## Spelobia 8.121767e-01 0.9955117434 FALSE 16
## Bunonema 4.376758e-01 0.7482844797 FALSE 16
## Aquatides 2.316162e-01 0.6888466043 FALSE 16
## Myolaimus 8.121767e-01 0.9955117434 FALSE 16
## Altainella 9.943724e-01 0.9998883537 FALSE 16
## Dicyrtomina 5.396760e-01 0.8412595983 FALSE 16
## Anguina 2.316162e-01 0.6888466043 FALSE 16
## Deladenus 7.915551e-01 0.9955117434 FALSE 16
## Geobacter 2.969467e-01 0.7352513393 FALSE 16
## NA. 9.060803e-01 0.9998883537 FALSE 16
## Odontocepheus 9.074258e-01 0.9998883537 FALSE 16
## Theristus 7.915551e-01 0.9955117434 FALSE 16
## Thonus 9.943724e-01 0.9998883537 FALSE 16
## Candidatus.Acetothermum 9.943724e-01 0.9998883537 FALSE 16
## Sellnickochthonius 8.121767e-01 0.9955117434 FALSE 16
##
##
## $covariance
## NULL
##
## $feature.dat.use
## 1 2 3 4
## Globodera 9.555057e-01 8.863991e-01 7.903606e-01 7.858771e-01
## Meloidogyne 2.861367e-04 3.180478e-04 1.298566e-01 1.009443e-02
## Pristionchus 1.449759e-02 7.745909e-02 1.233878e-02 4.214542e-02
## Rhabditis 7.916448e-03 6.129648e-03 3.989139e-02 3.940085e-02
## Nygolaimus 1.096857e-03 7.054878e-03 1.920696e-02 3.246965e-02
## Acrobeloides 2.146025e-03 7.517493e-04 1.677115e-03 7.442899e-04
## Aporcelaimellus 3.719777e-03 5.002024e-03 2.675398e-03 1.079220e-02
## Pellioditis 4.816634e-03 8.702943e-03 6.799946e-04 5.312369e-02
## Cephaloboides 1.047296e-04 3.180478e-03 2.795192e-04 7.442899e-04
## Eucephalobus 1.573752e-03 1.047296e-04 1.047296e-04 1.442062e-03
## Acrobeles 4.768945e-04 4.555394e-05 4.555394e-05 1.162953e-03
## Mesorhabditis 6.199628e-04 8.247649e-05 8.247649e-05 2.791087e-04
## Pratylenchus 7.100659e-05 7.100659e-05 1.118077e-03 5.582174e-04
## Hypsibius 3.815156e-04 8.095761e-04 2.795192e-04 6.833090e-05
## Aphelenchus 7.100659e-05 1.330018e-03 6.389011e-04 3.721450e-04
## Cruznema 2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Aphelenchoides 3.099814e-03 5.493552e-04 2.414224e-04 1.674652e-03
## Anaplectus 1.099687e-04 1.099687e-04 1.099687e-04 3.349305e-03
## Metateratocephalus 3.338261e-04 4.337015e-04 1.669131e-04 1.669131e-04
## Plectus 1.011970e-04 2.023940e-04 7.187637e-04 4.651812e-03
## Paramphidelus 1.597253e-04 1.597253e-04 3.194505e-04 5.116993e-04
## Filenchus 9.237753e-05 2.891343e-04 9.237753e-05 3.628413e-03
## Ditylenchus 1.559495e-04 5.204418e-04 1.559495e-04 1.721170e-03
## Sectonema 7.918074e-05 3.469612e-04 7.918074e-05 7.918074e-05
## Chiloplacus 3.416545e-04 3.416545e-04 3.416545e-04 1.535098e-03
## Bastiania 4.189184e-05 4.189184e-05 4.189184e-05 3.256268e-04
## Clarkus 2.861367e-04 1.156537e-04 5.782687e-05 6.977718e-04
## Trischistoma 5.722734e-04 1.156337e-04 1.156337e-04 8.838443e-04
## Tylencholaimus 1.924452e-04 1.924452e-04 6.389011e-04 1.924452e-04
## Zeldia 2.670609e-03 4.189184e-04 4.189184e-04 4.189184e-04
## Zeatylenchus 1.047296e-04 1.047296e-04 1.047296e-04 1.047296e-04
## Prismatolaimus 2.111486e-04 2.111486e-04 2.111486e-04 2.111486e-04
## Diploscapter 4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
## Prodesmodora 1.246417e-04 1.246417e-04 1.246417e-04 1.246417e-04
## Panagrolaimus 2.804437e-04 2.804437e-04 2.804437e-04 2.804437e-04
## Diphascon 9.303624e-05 9.303624e-05 9.303624e-05 1.860725e-04
## Mesaphorura 1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
## Tullbergia 4.337015e-05 8.674030e-05 4.337015e-05 4.337015e-05
## Spelobia 4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
## Bunonema 8.140671e-04 8.140671e-04 8.140671e-04 1.628134e-03
## Aquatides 6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
## Myolaimus 4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
## Altainella 3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
## Dicyrtomina 2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Anguina 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
## Deladenus 1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
## Geobacter 1.590239e-04 3.180478e-04 1.590239e-04 1.590239e-04
## NA. 1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
## Odontocepheus 1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
## Theristus 1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
## Thonus 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Sellnickochthonius 2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
## 5 6 7 8
## Globodera 7.743274e-01 7.720015e-01 7.663992e-01 7.303019e-01
## Meloidogyne 5.805581e-02 2.442002e-03 4.451698e-02 1.048001e-01
## Pristionchus 3.955396e-02 2.855264e-02 1.336869e-01 2.243361e-02
## Rhabditis 7.550755e-02 9.711656e-02 2.180516e-02 6.367185e-02
## Nygolaimus 2.100210e-03 1.131774e-02 5.893286e-04 1.044702e-03
## Acrobeloides 1.700170e-03 1.737579e-03 1.994651e-03 1.814483e-03
## Aporcelaimellus 6.650665e-03 1.709402e-02 5.802620e-03 6.983010e-03
## Pellioditis 1.105111e-02 2.503053e-02 1.359989e-03 4.701160e-02
## Cephaloboides 1.200120e-03 1.784540e-03 4.487964e-03 2.749216e-04
## Eucephalobus 2.200220e-03 6.574622e-04 1.047296e-04 1.110683e-02
## Acrobeles 1.600160e-03 4.555394e-05 3.173308e-03 4.555394e-05
## Mesorhabditis 8.247649e-05 4.696159e-04 1.178657e-03 1.649530e-04
## Pratylenchus 3.500350e-04 2.817695e-04 8.159935e-04 7.100659e-05
## Hypsibius 3.500350e-04 2.348079e-04 1.813319e-04 6.833090e-05
## Aphelenchus 3.000300e-04 7.100659e-05 7.100659e-05 7.100659e-05
## Cruznema 2.816901e-04 3.052503e-03 1.128791e-02 1.209655e-03
## Aphelenchoides 2.000200e-03 2.414224e-04 1.133324e-03 3.848903e-03
## Anaplectus 3.500350e-03 1.099687e-04 1.269323e-03 2.199373e-04
## Metateratocephalus 7.150715e-03 3.287311e-03 1.669131e-04 1.669131e-04
## Plectus 1.900190e-03 6.574622e-04 1.011970e-04 1.011970e-04
## Paramphidelus 5.500550e-04 1.690617e-03 1.597253e-04 1.597253e-04
## Filenchus 9.237753e-05 9.237753e-05 9.237753e-05 9.237753e-05
## Ditylenchus 3.650365e-03 1.559495e-04 1.559495e-04 3.848903e-04
## Sectonema 7.918074e-05 3.057199e-02 7.918074e-05 7.918074e-05
## Chiloplacus 8.500850e-04 3.416545e-04 3.416545e-04 1.264640e-03
## Bastiania 4.189184e-05 9.392317e-05 4.189184e-05 4.189184e-05
## Clarkus 5.782687e-05 1.878463e-04 5.782687e-05 5.782687e-05
## Trischistoma 1.156337e-04 1.156337e-04 1.156337e-04 3.079122e-03
## Tylencholaimus 3.050305e-03 1.924452e-04 1.924452e-04 3.848903e-04
## Zeldia 4.189184e-04 4.189184e-04 4.189184e-04 4.189184e-04
## Zeatylenchus 2.400240e-03 1.047296e-04 1.047296e-04 1.047296e-04
## Prismatolaimus 2.111486e-04 2.111486e-04 2.111486e-04 2.111486e-04
## Diploscapter 4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
## Prodesmodora 1.246417e-04 1.246417e-04 1.246417e-04 1.246417e-04
## Panagrolaimus 2.804437e-04 2.804437e-04 2.804437e-04 2.804437e-04
## Diphascon 9.303624e-05 9.303624e-05 9.303624e-05 9.303624e-05
## Mesaphorura 1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
## Tullbergia 4.337015e-05 4.337015e-05 4.337015e-05 4.337015e-05
## Spelobia 4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
## Bunonema 8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
## Aquatides 6.809430e-04 1.361886e-03 6.809430e-04 6.809430e-04
## Myolaimus 4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
## Altainella 3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
## Dicyrtomina 2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Anguina 1.878463e-04 3.756927e-04 1.878463e-04 1.878463e-04
## Deladenus 1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
## Geobacter 1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
## NA. 1.586654e-04 1.586654e-04 3.173308e-04 1.586654e-04
## Odontocepheus 1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
## Theristus 1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
## Thonus 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Sellnickochthonius 2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
## 9 10 11 12
## Globodera 7.288732e-01 7.262720e-01 6.823507e-01 6.584559e-01
## Meloidogyne 3.880282e-02 1.998612e-01 1.409697e-01 1.735998e-01
## Pristionchus 1.004225e-01 2.835338e-02 8.288670e-02 1.135269e-02
## Rhabditis 6.035211e-02 1.845513e-02 5.421912e-02 3.682292e-02
## Nygolaimus 1.901408e-03 1.433858e-03 9.971332e-04 1.675673e-03
## Acrobeloides 5.070423e-03 2.543941e-03 4.424779e-03 1.390809e-02
## Aporcelaimellus 2.014085e-02 7.493062e-03 6.855291e-03 5.990532e-03
## Pellioditis 2.894366e-02 5.134135e-03 5.733516e-03 9.174312e-02
## Cephaloboides 1.971831e-03 8.325624e-04 1.283809e-02 2.094592e-04
## Eucephalobus 9.154930e-04 4.162812e-04 3.739250e-04 2.094592e-04
## Acrobeles 2.535211e-03 6.197965e-03 6.855291e-04 5.864857e-04
## Mesorhabditis 3.521127e-04 8.247649e-05 9.348124e-04 8.247649e-05
## Pratylenchus 7.100659e-05 1.850139e-04 9.971332e-04 7.100659e-05
## Hypsibius 6.833090e-05 1.850139e-04 6.833090e-05 6.833090e-05
## Aphelenchus 2.816901e-04 4.162812e-04 7.100659e-05 5.864857e-04
## Cruznema 5.633803e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Aphelenchoides 2.414224e-04 2.414224e-04 2.414224e-04 2.414224e-04
## Anaplectus 9.859155e-04 1.099687e-04 6.855291e-04 1.099687e-04
## Metateratocephalus 6.338028e-04 4.625347e-04 1.669131e-04 3.770265e-04
## Plectus 3.028169e-03 1.011970e-04 4.362458e-04 1.011970e-04
## Paramphidelus 1.126761e-03 1.597253e-04 1.597253e-04 1.885133e-03
## Filenchus 9.154930e-04 2.775208e-04 3.116041e-04 2.513510e-04
## Ditylenchus 8.450704e-04 1.559495e-04 1.559495e-04 1.559495e-04
## Sectonema 7.918074e-05 2.775208e-04 7.918074e-05 7.918074e-05
## Chiloplacus 3.416545e-04 3.416545e-04 3.416545e-04 3.416545e-04
## Bastiania 7.746479e-04 1.387604e-04 4.189184e-05 8.378367e-05
## Clarkus 5.782687e-05 5.782687e-05 5.782687e-05 5.782687e-05
## Trischistoma 1.156337e-04 2.312673e-04 1.156337e-04 1.156337e-04
## Tylencholaimus 1.924452e-04 1.924452e-04 1.924452e-04 1.924452e-04
## Zeldia 4.189184e-04 4.189184e-04 2.929079e-03 8.378367e-04
## Zeatylenchus 1.047296e-04 3.700278e-04 1.047296e-04 2.094592e-04
## Prismatolaimus 2.111486e-04 4.625347e-04 5.608874e-04 2.111486e-04
## Diploscapter 4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
## Prodesmodora 1.246417e-04 1.246417e-04 2.492833e-04 1.246417e-04
## Panagrolaimus 2.804437e-04 2.804437e-04 5.608874e-04 2.804437e-04
## Diphascon 9.303624e-05 9.303624e-05 9.303624e-05 9.303624e-05
## Mesaphorura 1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
## Tullbergia 4.337015e-05 4.337015e-05 4.337015e-05 1.256755e-04
## Spelobia 4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
## Bunonema 8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
## Aquatides 6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
## Myolaimus 4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
## Altainella 3.770265e-04 3.770265e-04 3.770265e-04 7.540530e-04
## Dicyrtomina 5.633803e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Anguina 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
## Deladenus 1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
## Geobacter 1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
## NA. 1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
## Odontocepheus 1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
## Theristus 1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
## Thonus 8.378367e-05 8.378367e-05 8.378367e-05 1.675673e-04
## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 1.675673e-04
## Sellnickochthonius 2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
## 13 14 15 16
## Globodera 6.070910e-01 5.876176e-01 5.180925e-01 6.273754e-02
## Meloidogyne 2.026938e-02 3.679888e-01 2.289820e-01 6.050728e-01
## Pristionchus 2.396012e-01 9.356968e-03 7.322200e-02 1.390942e-01
## Rhabditis 6.727849e-02 1.398347e-02 1.427800e-01 7.242399e-02
## Nygolaimus 1.716625e-03 1.299579e-03 3.976369e-04 3.035262e-03
## Acrobeloides 9.573485e-03 1.871394e-03 5.850943e-03 2.190667e-02
## Aporcelaimellus 4.489634e-03 5.926080e-03 1.334924e-03 1.470122e-02
## Pellioditis 4.417008e-02 8.785154e-03 1.448534e-02 9.976774e-03
## Cephaloboides 3.961442e-04 1.047296e-04 9.259259e-03 1.805321e-02
## Eucephalobus 2.640961e-04 2.599158e-04 1.047296e-04 6.598395e-04
## Acrobeles 1.254457e-03 1.507512e-03 4.555394e-05 3.827069e-03
## Mesorhabditis 5.281923e-04 8.247649e-05 1.050898e-03 5.225929e-03
## Pratylenchus 9.243365e-04 7.100659e-05 1.420132e-04 2.111486e-03
## Hypsibius 2.640961e-04 6.833090e-05 3.976369e-04 1.821157e-03
## Aphelenchus 7.100659e-05 7.100659e-05 1.420132e-04 6.070524e-04
## Cruznema 1.320481e-03 2.816901e-04 1.107703e-03 9.633657e-03
## Aphelenchoides 2.414224e-04 2.414224e-04 4.828448e-04 1.055743e-03
## Anaplectus 1.099687e-04 1.099687e-04 1.099687e-04 4.750845e-04
## Metateratocephalus 1.669131e-04 1.669131e-04 1.669131e-04 1.583615e-03
## Plectus 1.011970e-04 1.011970e-04 1.011970e-04 1.011970e-04
## Paramphidelus 1.597253e-04 7.797474e-04 1.597253e-04 6.519215e-03
## Filenchus 9.237753e-05 9.237753e-05 9.237753e-05 1.847551e-04
## Ditylenchus 5.942163e-04 3.118989e-04 1.559495e-04 6.334459e-04
## Sectonema 2.640961e-04 7.918074e-05 7.918074e-05 1.583615e-04
## Chiloplacus 3.416545e-04 3.416545e-04 1.278119e-03 1.847551e-03
## Bastiania 4.189184e-05 4.189184e-05 4.189184e-05 4.486909e-04
## Clarkus 5.782687e-05 5.782687e-05 1.704158e-04 5.782687e-05
## Trischistoma 1.156337e-04 1.156337e-04 1.156337e-04 1.741976e-03
## Tylencholaimus 1.924452e-04 1.924452e-04 1.924452e-04 5.278716e-04
## Zeldia 4.189184e-04 4.189184e-04 4.189184e-04 4.189184e-04
## Zeatylenchus 1.047296e-04 1.047296e-04 1.047296e-04 1.047296e-04
## Prismatolaimus 2.111486e-04 2.111486e-04 2.111486e-04 4.222973e-04
## Diploscapter 4.118382e-04 4.118382e-04 8.236764e-04 4.118382e-04
## Prodesmodora 1.246417e-04 1.246417e-04 1.246417e-04 1.425253e-03
## Panagrolaimus 2.804437e-04 2.804437e-04 2.804437e-04 6.862331e-04
## Diphascon 9.303624e-05 9.303624e-05 9.303624e-05 4.750845e-04
## Mesaphorura 1.451647e-04 1.451647e-04 1.451647e-04 2.903294e-04
## Tullbergia 4.337015e-05 4.337015e-05 4.337015e-05 4.337015e-05
## Spelobia 4.856419e-03 4.856419e-03 4.856419e-03 9.712838e-03
## Bunonema 8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
## Aquatides 6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
## Myolaimus 4.354941e-04 4.354941e-04 4.354941e-04 8.709882e-04
## Altainella 3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
## Dicyrtomina 2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
## Anguina 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
## Deladenus 1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
## Geobacter 1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
## NA. 1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
## Odontocepheus 1.559495e-04 3.118989e-04 1.559495e-04 1.559495e-04
## Theristus 1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
## Thonus 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
## Sellnickochthonius 2.639358e-05 2.639358e-05 2.639358e-05 5.278716e-05
## 17 18
## Globodera 2.338695e-01 2.761935e-01
## Meloidogyne 1.566616e-01 3.619716e-01
## Pristionchus 3.462822e-01 9.648324e-02
## Rhabditis 5.256449e-02 1.953808e-01
## Nygolaimus 5.827011e-03 1.002187e-03
## Acrobeloides 7.465857e-03 6.195335e-03
## Aporcelaimellus 8.376328e-03 6.741983e-03
## Pellioditis 4.564492e-02 1.093294e-02
## Cephaloboides 8.758725e-02 3.325437e-03
## Eucephalobus 3.095599e-03 1.047296e-04
## Acrobeles 9.711684e-04 9.110787e-05
## Mesorhabditis 4.309560e-03 2.186589e-03
## Pratylenchus 8.497724e-04 8.655248e-04
## Hypsibius 6.833090e-05 1.366618e-04
## Aphelenchus 1.396055e-02 7.100659e-05
## Cruznema 1.566009e-02 2.291363e-02
## Aphelenchoides 2.414224e-04 4.145408e-03
## Anaplectus 9.711684e-04 1.685496e-03
## Metateratocephalus 1.669131e-04 1.138848e-03
## Plectus 6.069803e-04 2.049927e-03
## Paramphidelus 1.597253e-04 1.597253e-04
## Filenchus 9.237753e-05 8.199708e-04
## Ditylenchus 1.559495e-04 1.559495e-04
## Sectonema 4.855842e-04 4.555394e-04
## Chiloplacus 1.760243e-03 6.833090e-04
## Bastiania 4.189184e-05 4.189184e-05
## Clarkus 1.213961e-04 5.782687e-05
## Trischistoma 1.156337e-04 1.156337e-04
## Tylencholaimus 1.924452e-04 1.924452e-04
## Zeldia 4.189184e-04 2.687682e-03
## Zeatylenchus 1.274659e-03 1.047296e-04
## Prismatolaimus 2.111486e-04 1.047741e-03
## Diploscapter 1.165402e-02 4.118382e-04
## Prodesmodora 1.246417e-04 1.246417e-04
## Panagrolaimus 2.804437e-04 2.804437e-04
## Diphascon 9.303624e-05 9.303624e-05
## Mesaphorura 1.451647e-04 3.188776e-04
## Tullbergia 4.337015e-05 4.337015e-05
## Spelobia 4.856419e-03 4.856419e-03
## Bunonema 8.140671e-04 8.140671e-04
## Aquatides 6.809430e-04 6.809430e-04
## Myolaimus 4.354941e-04 4.354941e-04
## Altainella 3.770265e-04 3.770265e-04
## Dicyrtomina 2.816901e-04 2.816901e-04
## Anguina 1.878463e-04 1.878463e-04
## Deladenus 1.594388e-04 3.188776e-04
## Geobacter 1.590239e-04 1.590239e-04
## NA. 1.586654e-04 1.586654e-04
## Odontocepheus 1.559495e-04 1.559495e-04
## Theristus 1.138848e-04 2.277697e-04
## Thonus 8.378367e-05 8.378367e-05
## Candidatus.Acetothermum 8.378367e-05 8.378367e-05
## Sellnickochthonius 2.639358e-05 2.639358e-05
##
## $meta.dat.use
## ndvi_01
## 1 -1.1410253
## 2 -1.9625407
## 3 0.5747102
## 4 -1.0529250
## 5 -0.7571646
## 6 -1.9896865
## 7 0.5945529
## 8 0.3130208
## 9 -0.8421145
## 10 0.5095020
## 11 0.6619353
## 12 0.3558477
## 13 0.6228720
## 14 0.6121273
## 15 0.9980715
## 16 0.6887884
## 17 0.9838132
## 18 0.8302155
##
## $wald
## $wald$beta
## Globodera Meloidogyne Pristionchus Rhabditis Nygolaimus
## (Intercept) 10.1047233 6.496183 6.6186168 6.3658080 2.2073429
## ndvi_01 -0.4738524 2.747166 0.2991004 0.4253496 -0.8606651
## Acrobeloides Aporcelaimellus Pellioditis Cephaloboides Eucephalobus
## (Intercept) 2.6788599 3.6747802 4.6182753 1.5630598 -0.08481149
## ndvi_01 0.7763032 -0.3585514 -0.2782907 0.5733044 -0.43302585
## Acrobeles Mesorhabditis Pratylenchus Hypsibius Aphelenchus
## (Intercept) -0.07274706 -0.3862146 -0.8045352 -1.4961972 -1.114326359
## ndvi_01 0.50561441 0.6668211 0.6661323 -0.3072165 0.007409314
## Cruznema Aphelenchoides Anaplectus Metateratocephalus Plectus
## (Intercept) 0.9927140 0.2624983 -0.57883414 -0.3524108 -0.6559045
## ndvi_01 0.7845971 -0.2341110 -0.01794904 -0.7686285 -0.7034979
## Paramphidelus Filenchus Ditylenchus Sectonema Chiloplacus
## (Intercept) -0.4603681 -1.4003298 -0.6860119 -1.5545764 0.1470745
## ndvi_01 -0.3519134 -0.3794304 -0.5639328 -0.8565931 0.2001239
## Bastiania Clarkus Trischistoma Tylencholaimus Zeldia
## (Intercept) -2.8025871 -2.5181936 -1.3150763 -0.9533033 0.22372041
## ndvi_01 -0.3546597 -0.5762215 -0.0988178 -0.0795614 0.04383992
## Zeatylenchus Prismatolaimus Diploscapter Prodesmodora Panagrolaimus
## (Intercept) -1.67711201 -0.9484702 0.01373187 -1.7831843 -0.73683560
## ndvi_01 0.04498005 0.2042330 0.29987259 0.1434408 0.05331597
## Diphascon Mesaphorura Tullbergia Spelobia Bunonema
## (Intercept) -2.269719357 -1.69550491 -3.4162203 3.305552544 0.72887925
## ndvi_01 -0.004556226 0.05803299 -0.1212424 0.002588669 -0.09986506
## Aquatides Myolaimus Altainella Dicyrtomina Anguina
## (Intercept) 0.4712656 -0.173615293 -0.38160243 -0.80215936 -1.3867154
## ndvi_01 -0.1549687 0.002588669 -0.01699608 -0.08746444 -0.1549687
## Deladenus Geobacter NA. Odontocepheus Theristus
## (Intercept) -1.62326589 -1.6270250 -1.630280895 -1.655189754 -2.10869272
## ndvi_01 0.01090791 -0.1533719 -0.002954595 -0.001920806 0.01090791
## Thonus Candidatus.Acetothermum Sellnickochthonius
## (Intercept) -2.55152743 -2.55152743 -4.218009412
## ndvi_01 -0.01699608 -0.01699608 0.002588669
##
## $wald$sig
## Globodera Meloidogyne Pristionchus
## 1.2342725 1.6541912 1.3613582
## Rhabditis Nygolaimus Acrobeloides
## 1.1193531 1.4884675 1.0139656
## Aporcelaimellus Pellioditis Cephaloboides
## 0.6831587 1.7946175 2.4592792
## Eucephalobus Acrobeles Mesorhabditis
## 1.8784209 2.4573375 1.7428159
## Pratylenchus Hypsibius Aphelenchus
## 1.4510802 1.4464847 1.9989081
## Cruznema Aphelenchoides Anaplectus
## 2.0341758 1.5922080 1.6969377
## Metateratocephalus Plectus Paramphidelus
## 1.3691205 1.7560392 1.5245705
## Filenchus Ditylenchus Sectonema
## 1.4308885 1.2144454 1.9432420
## Chiloplacus Bastiania Clarkus
## 0.7629270 1.1888784 0.9725051
## Trischistoma Tylencholaimus Zeldia
## 1.5303270 1.0472285 1.1884082
## Zeatylenchus Prismatolaimus Diploscapter
## 1.3066891 0.6499247 1.0620379
## Prodesmodora Panagrolaimus Diphascon
## 0.7111901 0.4222793 0.4651313
## Mesaphorura Tullbergia Spelobia
## 0.3638551 0.6633789 0.3495281
## Bunonema Aquatides Myolaimus
## 0.4246029 0.4545341 0.3495281
## Altainella Dicyrtomina Anguina
## 0.5147803 0.4577093 0.4545341
## Deladenus Geobacter NA.
## 0.4418165 0.5181055 0.5137543
## Odontocepheus Theristus Thonus
## 0.5573384 0.4418165 0.5147803
## Candidatus.Acetothermum Sellnickochthonius
## 0.5147803 0.3495281
##
## $wald$X
## (Intercept) ndvi_01
## 1 1 -1.1410253
## 2 1 -1.9625407
## 3 1 0.5747102
## 4 1 -1.0529250
## 5 1 -0.7571646
## 6 1 -1.9896865
## 7 1 0.5945529
## 8 1 0.3130208
## 9 1 -0.8421145
## 10 1 0.5095020
## 11 1 0.6619353
## 12 1 0.3558477
## 13 1 0.6228720
## 14 1 0.6121273
## 15 1 0.9980715
## 16 1 0.6887884
## 17 1 0.9838132
## 18 1 0.8302155
## attr(,"assign")
## [1] 0 1
##
## $wald$bias
## [1] -1.00756447 -0.01789055
# 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
## $plot.volcano[[1]]
# Write supplementary table for manuscript
l_model_df <- as.data.frame(l_model$output)
write.xlsx(l_model_df, file = "supplementary_table_ndvi_regression_nematodes.xlsx", rowNames = TRUE, colnames = TRUE)
# Prat
set$prat_data$ndvi_temp <- (set$prat_data$ndvi+1)/2
model_prat_count_ndvi = glm(
ndvi_temp ~ total_count,
data = set$prat_data,
family = quasibinomial
)
summary(model_prat_count_ndvi)
##
## Call:
## glm(formula = ndvi_temp ~ total_count, family = quasibinomial,
## data = set$prat_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.263912 0.203506 1.297 0.2131
## total_count 0.011331 0.004609 2.459 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.0613813)
##
## Null deviance: 1.4028 on 17 degrees of freedom
## Residual deviance: 1.0038 on 16 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
# Glob
set$glob_data$ndvi_temp <- (set$glob_data$ndvi+1)/2
model_glob_count_ndvi = glm(
ndvi_temp ~ total_count,
data = set$glob_data,
family = quasibinomial
)
summary(model_glob_count_ndvi)
##
## Call:
## glm(formula = ndvi_temp ~ total_count, family = quasibinomial,
## data = set$glob_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.149679 0.287632 3.997 0.00104 **
## total_count -0.004626 0.002444 -1.893 0.07662 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.07214207)
##
## Null deviance: 1.4028 on 17 degrees of freedom
## Residual deviance: 1.1402 on 16 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
# Mel
set$mel_data$ndvi_temp <- (set$mel_data$ndvi+1)/2
model_mel_count_ndvi = glm(
ndvi_temp ~ total_count,
data = set$mel_data,
family = quasibinomial
)
summary(model_mel_count_ndvi)
##
## Call:
## glm(formula = ndvi_temp ~ total_count, family = quasibinomial,
## data = set$mel_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1416789 0.1490480 0.951 0.355967
## total_count 0.0024307 0.0005518 4.405 0.000443 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.0369228)
##
## Null deviance: 1.40277 on 17 degrees of freedom
## Residual deviance: 0.59886 on 16 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
data_mol_morph_long_temp <- pivot_longer(set$data_mol_morph,
cols = c(Genus_Sample_Abundance,
total_count),
names_to = "type",
values_to = "value")
data_mol_morph_long_temp$ndvi <- (data_mol_morph_long_temp$ndvi+1)/2
pval_glm <- function(glm){
coef(summary(glm))[,4][2]
}
set$data_mol_morph_long <- data_mol_morph_long_temp %>%
mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob), NA)) %>%
mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat), pval)) %>%
mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel), pval)) %>%
mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
# Count data vs NDVI
plots$morph_count_ndvi <- ggplot(set$data_mol_morph_long,
aes(x = value,
y = ndvi)) +
geom_point(alpha = 0.7) +
#ggtitle(set$plot_title) +
theme(plot.title = element_text(hjust = 0.5, size = 20),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "dark grey"),
strip.background = element_blank(),
strip.text.x = element_text(face = "italic")#,
#axis.text = element_text(size = 12),
#text = element_text(size = 14)
) +
#xlab("Nematodes / 250 ml soil") +
#ylab("NDVI") +
geom_smooth(method = "glm", formula = set$my_formula, se = F, color = "dark grey",
method.args = list(family = quasibinomial)) +
facet_wrap(~Genus, scales = "free_x")
plots$morph_count_ndvi
# Print and save plot
plots$morph_count_ndvi
save_plot(plots$morph_count_ndvi, plot_name = paste0("morph_count_ndvi"),
filetype = tmp$out)
set$data_mol_morph_long <- data_mol_morph_long_temp %>%
mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob_count_ndvi), NA)) %>%
mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat_count_ndvi), pval)) %>%
mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel_count_ndvi), pval)) %>%
mutate(pval = ifelse(Genus == "Globodera" & type == "Genus_Sample_Abundance", 0.5574120890, pval)) %>%
mutate(pval = ifelse(Genus == "Pratylenchus" & type == "Genus_Sample_Abundance", 0.5177548997, pval)) %>%
mutate(pval = ifelse(Genus == "Meloidogyne" & type == "Genus_Sample_Abundance", 0.0001920515, pval)) %>%
mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
# Morph data
set$data_morph <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'total_count',]
set$data_morph <- set$data_morph %>%
dplyr::rename("Number_of_nematodes" = value)
# Relative abundance data
set$data_relabu <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'Genus_Sample_Abundance',]
set$data_relabu <- set$data_relabu %>%
dplyr::rename("Relative_abundance" = value)
signif.labs <- c("pval > 0.05", "pval <= 0.05")
names(signif.labs) <- c("p value > 0.05", "p value <= 0.05")
breaks_morph <- function(x) { if (max(x) < 200) seq (0, 100, 100) else seq(0, 600, 50)}
breaks_mb <- function(x) { if (max(x) < 0.1) seq(0, 0.002, 3) else seq(0, 3, 1) }
breaks_fun_morph <- function(x) {
if (max(x) > 500) {
seq(0, 800, 200)
} else if (max(x) > 150) {
seq(0, 200, length.out = 5)
} else {
seq(0, 100, length.out = 5)
}}
# MORPHOLOGIAL AND NDVI PLOT
# Relative abundance and NDVI plotting
# Axis ticks MB
breaks_fun_mb <- function(x) {
if (max(x) > 0.8) {
seq(0, 0.9, length.out = 4)
} else if (max(x) > 0.2) {
seq(0, 0.6, 0.2)
} else {
seq(0, 0.003, length.out = 4)
}}
p1 <- ggplot(set$data_relabu,
aes(x = Relative_abundance,
y = ndvi)) +
geom_point(alpha = 0.7, color = "black") +
ggtitle("a)") +
theme(plot.title = element_text(hjust = 0, size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
strip.background = element_blank(),
axis.title = element_text(size = 9),
strip.text.x = element_text(face = "italic"),
plot.caption = element_text(size = 7, hjust = 0, face = "italic",
margin = margin(t = 0.2, unit = "cm")),
plot.caption.position = "plot",
plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5,
margin = margin(b = 0.8, unit = "cm"))) +
xlab("Relative abundance") +
ylab("NDVI") +
geom_smooth(method = "glm", formula = set$my_formula, se = FALSE,
method.args = list(family = quasibinomial),
aes(color = signif, linetype = signif)) +
scale_color_manual(values=c('black', 'lightgrey'),
name= 'Significance',
labels = names(signif.labs)) +
scale_linetype_discrete(name= 'Significance',
labels = names(signif.labs)) +
facet_wrap(~Genus, scales = "free_x") +
scale_x_continuous(breaks = breaks_fun_mb, limits = c(0, NA))
p1
# Plotting NDVI vs metabarcoding
p2 <- ggplot(set$data_morph,
aes(x = Number_of_nematodes,
y = ndvi)) +
geom_point(alpha = 0.7, color = "black") +
ggtitle("b)") +
theme(plot.title = element_text(hjust = 0, size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
strip.background = element_blank(),
axis.title = element_text(size = 9),
panel.spacing=unit(2, "lines"),
strip.text.x = element_text(face = "italic"),
plot.caption = element_text(size = 7, hjust = 0, face = "italic",
margin = margin(t = 0.2, unit = "cm")),
plot.caption.position = "plot",
plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5,
margin = margin(b = 0.8, unit = "cm"))) +
xlab("Nematodes / 250 ml soil") +
ylab("NDVI") +
geom_smooth(method = "glm", formula = set$my_formula, se = FALSE,
method.args = list(family = quasibinomial),
aes(color = signif, linetype = signif)) +
scale_color_manual(values=c('black', 'lightgrey'),
name= 'Significance',
labels = names(signif.labs)) +
scale_linetype_discrete(name= 'Significance',
labels = names(signif.labs)) +
facet_wrap(~Genus, scales = "free_x") +
scale_x_continuous(breaks = breaks_fun_morph, limits = c(0, NA))
p2
# Combining the two plots
plots$combo_morph_metab_ndvi <- plot_grid(p1, p2,
ncol = 1,
align = "hv")
plots$combo_morph_metab_ndvi
# Print and save plot
save_plot(plots$combo_morph_metab_ndvi, plot_name = paste0("morph_relabu_ndvi"),
filetype = tmp$out)
found <- c(40, 24, 16, 37, 10, 27, 74, 100, 100, 78, 32, 28)
not_found <- c(24,12, 7, 7, 24, 52)
wilcox.test(found, not_found)
##
## Wilcoxon rank sum test with continuity correction
##
## data: found and not_found
## W = 58, p-value = 0.04339
## alternative hypothesis: true location shift is not equal to 0
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 cowplot_1.1.3
## [19] ggpubr_0.6.0 viridis_0.6.5 viridisLite_0.4.2
## [22] phangorn_2.12.1 biomformat_1.34.0 vegan_2.6-8
## [25] lattice_0.22-6 permute_0.9-7 scales_1.3.0
## [28] gridExtra_2.3 ape_5.8-1 reshape2_1.4.4
## [31] phyloseq_1.50.0 ggplot2_3.5.1 cuphyr_0.3
## [34] DECIPHER_3.2.0 Biostrings_2.74.0 GenomeInfoDb_1.42.1
## [37] XVector_0.46.0 IRanges_2.40.0 S4Vectors_0.44.0
## [40] BiocGenerics_0.52.0 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] vroom_1.6.5 car_3.1-3
## [33] zip_2.3.1 Matrix_1.7-1
## [35] interp_1.1-6 abind_1.4-8
## [37] lifecycle_1.0.4 yaml_2.3.10
## [39] carData_3.0-5 SummarizedExperiment_1.36.0
## [41] rhdf5_2.50.0 SparseArray_1.6.0
## [43] grid_4.4.2 promises_1.3.2
## [45] crayon_1.5.3 pwalign_1.2.0
## [47] miniUI_0.1.1.1 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] bit64_4.5.2 fBasics_4041.97
## [77] bslib_0.8.0 rpart_4.1.23
## [79] colorspace_2.1-1 DBI_1.2.3
## [81] nnet_7.3-19 ade4_1.7-22
## [83] tidyselect_1.2.1 timeSeries_4041.111
## [85] bit_4.5.0.1 compiler_4.4.2
## [87] curl_6.0.1 SparseM_1.84-2
## [89] DelayedArray_0.32.0 checkmate_2.3.2
## [91] lmtest_0.9-40 quadprog_1.5-8
## [93] spatial_7.3-17 digest_0.6.37
## [95] minqa_1.2.8 rmarkdown_2.29
## [97] htmltools_0.5.8.1 pkgconfig_2.0.3
## [99] jpeg_0.1-10 lme4_1.1-35.5
## [101] MatrixGenerics_1.18.0 stabledist_0.7-2
## [103] fastmap_1.2.0 rlang_1.1.4
## [105] htmlwidgets_1.6.4 UCSC.utils_1.2.0
## [107] shiny_1.10.0 farver_2.1.2
## [109] jquerylib_0.1.4 zoo_1.8-12
## [111] jsonlite_1.8.9 BiocParallel_1.40.0
## [113] magrittr_2.0.3 polynom_1.4-1
## [115] modeltools_0.2-23 Formula_1.2-5
## [117] GenomeInfoDbData_1.2.13 Rhdf5lib_1.28.0
## [119] munsell_0.5.1 stringi_1.8.4
## [121] stable_1.1.6 zlibbioc_1.52.0
## [123] MASS_7.3-61 plyr_1.8.9
## [125] pkgbuild_1.4.5 flexmix_2.3-19
## [127] ggrepel_0.9.6 deldir_2.0-4
## [129] splines_4.4.2 multtest_2.62.0
## [131] hms_1.1.3 igraph_2.1.2
## [133] ggsignif_0.6.4 rmutil_1.1.10
## [135] pkgload_1.4.0 evaluate_1.0.1
## [137] latticeExtra_0.6-30 RcppParallel_5.1.9
## [139] nloptr_2.1.1 tzdb_0.4.0
## [141] foreach_1.5.2 httpuv_1.6.15
## [143] MatrixModels_0.5-3 clue_0.3-66
## [145] broom_1.0.7 xtable_1.8-4
## [147] rstatix_0.7.2 later_1.4.1
## [149] ragg_1.3.3 lmerTest_3.1-3
## [151] memoise_2.0.1 GenomicAlignments_1.42.0
## [153] cluster_2.1.8 timechange_0.3.0