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Muskox_microbial_ecology_impact.R
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#Code written by M. R. Aggerbeck.
#mrag@envs.au.dk
#Creative commons.
#Published article:
#Muskoxen homogenise soil microbial communities and affect the abundance of methanogens and methanotrophs
#Aggerbeck et al., 2022
#10.1016/j.scitotenv.2022.153877
###----------------PACKAGES--------------
#Packages used in the following code.
library("phyloseq"); packageVersion("phyloseq")
library("devtools"); packageVersion("devtools")
library("biomformat"); packageVersion("biomformat")
library("vegan"); packageVersion("vegan")
library("ggplot2"); packageVersion("ggplot2")
library("DESeq2"); packageVersion("DESeq2")
library("mvabund"); packageVersion("mvabund")
library("metacoder"); packageVersion("metacoder")
library("taxa"); packageVersion("taxa")
library("microbiomeSeq"); packageVersion("microbiomeSeq")
library("adespatial"); packageVersion("adespatial")
library("ggpubr"); packageVersion("ggpubr")
library("data.table"); packageVersion("data.table")
library("igraph"); packageVersion("igraph")
library("dplyr"); packageVersion("dplyr")
library("tidyr"); packageVersion("tidyr")
library("plotrix"); packageVersion("plotrix")
library("microbiome"); packageVersion("microbiome")
#This page of code is using Phyloseq as the primary package, and uses the others above as additional analyses.
#Check out the Phyloseq tutorial and the various package manuals for more info on analyses.
#Set working directory
setwd("D:/Marie/Dropbox/Muskox_soil_microbial/WD/")
#Set user directory
uzdir <- "D:/Marie/Dropbox/Muskox_soil_microbial/WD/"
###---------------------LOAD DATA-------------------------------------
#Biom file to import - preferably JSON
#Dataset
data_biom <- "otu_table_sintax_rar.biom"
#Import data, removing any blank spaces in file path
biom_file <- paste(uzdir, data_biom, sep="")
#the actual act of importing the file into R
biom_otu_tax <- import_biom(biom_file)
colnames(tax_table(biom_otu_tax)) = c("Kingdom","Phylum","Class","Order","Family","Genus", "Species")
#inspect tax_table
head(tax_table(biom_otu_tax))
tax_table(biom_otu_tax) <- gsub(":", "_", tax_table(biom_otu_tax))
tax_table(biom_otu_tax) <- gsub("_sp._", "_sp_", tax_table(biom_otu_tax))
head(tax_table(biom_otu_tax))
head(otu_table(biom_otu_tax))
#Import metadata file, removing blank spaces in path
md_file <- paste(uzdir, "mapping_file2.txt", sep="")
#Has to be tsv. If csv, use code below.
metadata <- import_qiime_sample_data(md_file)
metadata
###--------------Data preparation---------------
# #If the above doesn't produce a table, use this:
# metadata <- read.csv(md_file, header = TRUE, sep = ",", quote = "\"", dec = ".", fill = TRUE, comment.char = "")
#change names in metadata to fit proper nomenclature
temp <- as.matrix(metadata)
temp <- gsub("-", "_", temp)
temp
metadata <- temp
metadata <- as.data.frame(metadata)
metadata <- sample_data(metadata)
head(metadata)
#Merge OTU data and metadata to single object - The Phyloseq Object(tm)
physeq <- merge_phyloseq(biom_otu_tax,metadata)
sample_names(physeq) <- gsub("-", "_", sample_names(physeq))
sample_names(physeq)
#physeq is now the s4 object - the phyloseq object - we will use throughout the rest of the code.
#In theory, all other objects in the global environment could be removed now, but I tend to leave them, just in case I need them.
head(sample_data(physeq))
head(tax_table(physeq))
head(otu_table(physeq))
#C4-5, 16-Tue_S16 is contaminated, proceed to remove from dataset.
physeq = subset_samples(physeq, Sample_Name != "C4_5")
metadata = subset_samples(metadata, Sample_Name != "C4_5")
#/data loaded
raw_data <- physeq
#physeq <- physeqa
#physeqa <- physeq
##Reset to raw data in case of error
#physeq <- raw_data
rank_names(physeq)
sample_names(physeq) = sample_data(physeq)$Sample_Name
tax_table(physeq)[is.na(tax_table(physeq))] <- "unidentified"
tax_table(physeq)[1:10]
tax_table(physeq)[,7] <- gsub("unidentified", "s_unidentified", tax_table(physeq)[,7]);
tax_table(physeq)[,6] <- gsub("unidentified", "g_unidentified", tax_table(physeq)[,6]);
tax_table(physeq)[,5] <- gsub("unidentified", "f_unidentified", tax_table(physeq)[,5]);
tax_table(physeq)[,4] <- gsub("unidentified", "o_unidentified", tax_table(physeq)[,4]);
tax_table(physeq)[,3] <- gsub("unidentified", "c_unidentified", tax_table(physeq)[,3]);
tax_table(physeq)[,2] <- gsub("unidentified", "p_unidentified", tax_table(physeq)[,2]);
physeq <- tax_glom(physeq, taxrank="Species"); physeq
length(unique(taxa_names(physeq)))
length(unique(tax_table(physeq)[,7]))
tax_table(physeq) <- gsub("s_unidentified", "", tax_table(physeq));
tax_table(physeq) <- gsub("g_unidentified", "", tax_table(physeq));
tax_table(physeq) <- gsub("f_unidentified", "", tax_table(physeq));
tax_table(physeq) <- gsub("o_unidentified", "", tax_table(physeq));
tax_table(physeq) <- gsub("c_unidentified", "", tax_table(physeq));
tax_table(physeq) <- gsub("p_unidentified", "", tax_table(physeq));
met <- as.matrix(sample_data(physeq))
renam <- as.matrix(sample_data(physeq2)[,6])
renam <- gsub("Control", "Grazed", renam)
renam <- gsub("Exclosure", "Ungrazed", renam)
met[,6] <- renam
met <- as.data.frame(met)
met <- sample_data(met)
sample_data(MBS_physeq) <- met
###-----------------Make unique names for taxa----------------------
mynames = NULL
for (i in 1:length(taxa_names(physeq))){
name <- makeTaxLabel(taxa_names(physeq)[i],physeq)
mynames <- rbind(mynames, c(name))
}
#Find duplicates
n_occur <- data.frame(table(mynames))
n_occur[n_occur$Freq > 1,]
taxa_names(physeq) = make.unique(mynames, sep="_")
taxa_names(physeq)[1:10]
physeq
sample_data(physeq)[1:5]
otu_table(physeq) [1:5]
tax_table(physeq)[1:15]
###------------------Filter out low-abundance reads-----------------
physeq10 <- physeq
otu_table(physeq10)[otu_table(physeq10)<10 ] <- 0
physeq10 = prune_taxa(taxa_sums(physeq10) > 0, physeq10)
physeq = prune_taxa(taxa_sums(physeq) > 0, physeq)
physeq10
physeq
###------------------SUBSETTING-------------------------------------
# #This for loop creates four subset from the fungicide treatments (other dataset).
# #We create a list of the names of the fungicides, and then use that to specify the subset command.
# #Then we paste the name of the fungicide onto the subset and assign it as a separate phyloseq object.
# ID<- as.vector(unique(sample_data(physeq)$Fungicide))
# for (i in 1:length(ID)){
# temp <- subset_samples(physeq, Fungicide==ID[i])
# temp = prune_taxa(taxa_sums(temp) > 0, temp)
# name <- paste(ID[i])
# assign(name, temp)
# }
#
# #Creates another set of subset, based on inoculation
# ID<- as.vector(unique(sample_data(physeq)$Inoculation))
# for (i in 1:length(ID)){
# temp <- subset_samples(physeq, Inoculation==ID[i])
# temp = prune_taxa(taxa_sums(temp) > 0, temp)
# name <- paste(ID[i])
# assign(name, temp)
# }
## Or you can make subsets of treatments. Specifies which metadata column, and which value in said column should be criteria.
# No_ino <- subset_samples(physeq, No_inoculation!="No")
# Control <- subset_samples(physeq, Fungicide=="Control")
#Cont_no_ino <- subset_samples(physeq, No_inoculation=="No_inoculationC")
###----------------------Merge samples----------------
temp = prune_taxa(taxa_sums(physeq) > 0, physeq)
temp2 <- as.data.frame(unique(sample_data(temp)[,5]))
groups_phy <- as.vector(as.character(temp2$Sample))
sample_data(temp)$group_phy <- get_variable(temp, "Sample") %in% groups_phy
merged_phy = merge_samples(temp, "Sample")
merged_phy
sample_names(temp)
sample_names(merged_phy)
sample_data(merged_phy)
sample_data(merged_phy)$Sample_Name = sample_names(merged_phy)
merged_phy
physeq
tmp <- sample_data(merged_phy)
class(tmp)
write.table(tmp, file='merged_metadata.tsv', quote=FALSE, sep='\t')
#It's a hassle to change every column in R - much faster to just copy-paste block metadata in excel.
#Do this, then return and import new file.
md_file <- paste(uzdir, "merged_metadata_alt.tsv", sep="")
meta <- import_qiime_sample_data(md_file)
otu <- otu_table(merged_phy)
tax <- tax_table(merged_phy)
temp <- merge_phyloseq(otu,tax,meta)
merged_phy <- temp
sample_data(merged_phy)
rm(otu,tax,temp,temp2,tmp)
###Merge unassigned taxa
sort(taxa_names(physeq))
#IMPORTANT! Input manually any unwanted higher classes from the list produced by the sort() above
badtaxa = c("k__Fungi", "o__Agaricales", "o__Coniochaetales", "o__Pleosporales", "p__Ascomycota", "p__Basidiomycota",
"c__Agaricomycetes", "c__Dothideomycetes", "c__Leotiomycetes", "c__Microbotryomycetes", "c__Sordariomycetes",
"c__Tremellomycetes")
phy_un_con <- merge_taxa(physeq, badtaxa)
taxa_names(phy_un_con) <- gsub("o__Agaricales", "Unassigned", taxa_names(phy_un_con))
head(tax_table(phy_un_con))
head(sample_data(phy_un_con))
sort(taxa_names(merged_phy))
badtaxa = c("k__Fungi", "o__Agaricales", "o__Coniochaetales", "o__Pleosporales", "p__Ascomycota", "p__Basidiomycota",
"c__Agaricomycetes","c__Microbotryomycetes", "c__Sordariomycetes", "c__Tremellomycetes")
phy_m_un_con <- merge_taxa(merged_phy, badtaxa)
taxa_names(phy_m_un_con) <- gsub("o__Agaricales", "Unassigned", taxa_names(phy_m_un_con))
head(tax_table(phy_m_un_con))
head(sample_data(phy_m_un_con))
sort(taxa_names(phy_m_un_con))
# ###----------Create unique names (?)---------
#
# temp = prune_taxa(taxa_sums(physeq) > 0, physeq)
# temp2 <- as.data.frame(unique(sample_data(temp)[,11]))
# groups_phy <- as.vector(as.character(temp2$Sample))
# sample_data(temp)$group_phy <- get_variable(temp, "Sample") %in% groups_phy
#
# merged_phy_2 = merge_samples(temp, "Treatment")
#
# merged_phy_2
# sample_names(temp)
# sample_names(merged_phy_2)
#
# sample_data(merged_phy_2)
# sample_data(merged_phy_2)$Sample_Name = sample_names(merged_phy_2)
# merged_phy_2
# physeq
###----------Filter by relative abundance-----------
physeq
#Filters out anything below 0.01% of total abundance.
minTotRelAbun = 0.01
x = taxa_sums(physeq)
keepTaxa = rownames(as.data.frame(which(((x / sum(x))*100) > minTotRelAbun)))
phy_filter1 = prune_taxa(keepTaxa, physeq)
y = as.matrix(sort((x / sum(x))*100, decreasing = TRUE))
x = taxa_sums(merged_phy)
keepTaxa = rownames(as.data.frame(which(((x / sum(x))*100) > minTotRelAbun)))
phy_m_filter1 = prune_taxa(keepTaxa, merged_phy)
###-----------------Colour palettes-----------------------
# I will from now on use a colour scheme that
set_2 <- c("royalblue","tomato")
set_3 <- c("royalblue", "grey95","tomato")
set_block <- c("cyan", "green","magenta", "gold")
set_fun <- c( "red", "violet", "brown","darkblue")
###----------------------Core biome----------------
#write.table(otu_table(physeq)[2], file='presence_pch.tsv', quote=FALSE, sep='\t')
write.table(y, file='taxalist_rel_ab.tsv', quote=FALSE, sep='\t')
core.taxa.standard <- core_members(physeq, detection = 0, prevalence = 75/100)
core.taxa.standard
write.table(core.taxa.standard, file='core_90.tsv', quote=FALSE, sep='\t')
core = prune_taxa(core.taxa.standard, physeq)
excl_phy = subset_samples(physeq, Treatment=="Exclosure")
excl_phy = prune_taxa(taxa_sums(excl_phy) > 0, excl_phy)
excl_phy
ctrl_phy = subset_samples(physeq, Treatment=="Control")
ctrl_phy = prune_taxa(taxa_sums(ctrl_phy) > 0, ctrl_phy)
ctrl_phy
allTaxa = taxa_names(physeq)
#myTaxa <- allTaxa[!(allTaxa %in% core.taxa.standard)]
ctrlTaxa <- taxa_names(ctrl_phy)
exclTaxa <- taxa_names(excl_phy)
myTaxa <- allTaxa[!(allTaxa %in% ctrlTaxa)]
unique_excl = prune_taxa(myTaxa, physeq)
unique_excl = prune_taxa(taxa_sums(unique_excl) > 0, unique_excl)
unique_excl
ns_unique_excl = prune_taxa
myTaxa <- allTaxa[!(allTaxa %in% exclTaxa)]
unique_ctrl = prune_taxa(myTaxa, physeq)
unique_ctrl = prune_taxa(taxa_sums(unique_ctrl) > 0, unique_ctrl)
unique_ctrl
###----------------------Make file for LEfSe---------------------
test <- physeq
tax_table(test)[is.na(tax_table(test))] <- "unidentified"
test <- tax_glom(test, taxrank="Species"); test
otu_lefse <- as.data.frame(otu_table(test))
tax_lefse <-tax_table(test)
met_lefse <-sample_data(test)
met_lefse <- as.matrix(met_lefse)
names_lefse <- paste(tax_lefse[,1],tax_lefse[,2],tax_lefse[,3],tax_lefse[4],tax_lefse[,5],tax_lefse[,6],tax_lefse[,7],"x", sep="å")
names_lefse <- gsub("s_", "", names_lefse);
names_lefse <- gsub("f_", "", names_lefse);
names_lefse <- gsub("g_", "", names_lefse);
names_lefse <- gsub("o_", "", names_lefse);
names_lefse <- gsub("c_", "", names_lefse);
names_lefse <- gsub("p_", "", names_lefse);
names_lefse <- gsub("k_", "", names_lefse);
names_lefse <- gsub("åååx", "", names_lefse);
names_lefse <- gsub("ååx", "", names_lefse);
names_lefse <- gsub("åx", "", names_lefse);
names_lefse <- gsub("å", "|", names_lefse);
head(names_lefse)
head(rownames(otu_lefse))
head(colnames(otu_lefse))
colnames_otu_lefse <- met_lefse[,8]
colnames(otu_lefse) = colnames_otu_lefse
length(names_lefse)
length(unique(names_lefse))
names_lefse_u <- make.unique(names_lefse)
head(names_lefse_u)
samdata_lefse <- rbind(met_lefse[,8],met_lefse[,6])
rownames(samdata_lefse) = cbind("sampleID", "Treatment")
otu_lefse <- as.matrix(otu_lefse)
rownames(otu_lefse) = names_lefse_u
temp <- rbind(samdata_lefse,otu_lefse)
head(otu_lefse)
head(temp)
write.table(temp, file='lefse_sintax_rar_taxglom_raw.tsv', quote=FALSE, sep='\t', col.names=FALSE)
###----------------------Make subset of significant lefse otus---------------------------------------
#load file from lefse
lefse_sign_raw <- as.character(as.matrix(LEfSe_sintax_rarefied_glom_raw_SIGN[,1]))
test
otu <- otu_table(test)
tax <- tax_table(test)
met <- sample_data(test)
temp <- rownames(tax)
tmp <- cbind(temp,tax)
head(tmp)
rownames(tmp) = names_lefse_u
head(tmp)[1]
tmp <- tax_table(tmp)
temp <- merge_phyloseq(otu,tmp,met)
temp
taxa_names(temp) = names_lefse_u
head(sample_data(temp))
head(tax_table(temp))
head(otu_table(temp))
subset_lefse <- prune_taxa(lefse_sign_raw, test)
subset_lefse
###----------------------PERMANOVA---------------------------
#Transform data to relative abundance ("compositional")
pseq.rel <- microbiome::transform(core, "compositional")
otu <- microbiome::abundances(pseq.rel)
meta <- microbiome::meta(pseq.rel)
p <- microbiome::plot_landscape(pseq.rel, method = "NMDS", distance = "jaccard", col = "Treatment", size = 3)
print(p)
permanova <- vegan::adonis(t(otu)~Treatment,
data = meta, permutations=999, method = "jaccard")
write.table(as.data.frame(permanova$aov.tab), file='permanova_jaccard_all.tsv', quote=FALSE, sep='\t')
print(as.data.frame(permanova$aov.tab))
dist <- vegan::vegdist(t(otu))
temp <- anova(vegan::betadisper(dist, meta$Treatment))
temp
write.table(temp, file='anova_treatment.tsv', quote=FALSE, sep='\t')
#Under construction
coef <- coefficients(permanova)
top.coef <- coef[rev(order(abs(coef)))[1:20]]
par(mar = c(3, 14, 2, 1))
barplot(sort(top.coef), horiz = T, las = 1, main = "Top taxa")
###-----------RICHNESS-----------
#This calculates richness for many difference diversity indices (check help file for specifics),
# but if calculated using microbiomeseq below, you get p-values on the figure as well.
#Provides a combined plot (ggplot2) of three diversity incides and evenness (InvSimpson)
#the geom_boxplot adds median and std handles to the plot.
#physeqrplot <- plot_richness(physeq, x="Inoculation", color="Fungicide", measures=c("Shannon", "Simpson", "InvSimpson"), title = "Richness, physeq dataset")
#physeqrplot + geom_boxplot(data = physeqrplot$data, aes(color = NULL), alpha = 0.1)
p_an <-plot_anova_diversity_pval(physeq2, method = c("richness","shannon", "evenness"),
grouping_column ="Treatment",pValueCutoff=0.05)
p_an_o <- p_an[[1]]; p_an_pvalues <- p_an[[2]]
p_an_o <- p_an_o + ggtitle("* = p<0.05, ** = p<0.01, *** = p<0.001") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank())
p <- p_an_o+scale_colour_manual(values = set_2, labels = c("Grazed ", "Ungrazed "))
p <- p + theme(legend.title = element_blank())+
theme(legend.position="bottom")
p
ggsave(p, file="richness_treatment.pdf")
write.table(p_an_pvalues, file='richness_treatment_pval.tsv', quote=FALSE, sep='\t')
write.table(p_an_o$data, file='richness_treatment_data.tsv', quote=FALSE, sep='\t')
#Blocks
p_an <-plot_anova_diversity_pval(physeq, method = c("shannon", "simpson", "evenness"),
grouping_column ="Sample",pValueCutoff=0.05)
p_an_o <- p_an[[1]]; p_an_pvalues <- p_an[[2]]
p_an_o <- p_an_o + ggtitle("Diversity index + evenness \n * = p<0.05, ** = p<0.01, *** = p<0.001") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank())
p_an_o
ggsave(p, file="richness_inoculation.pdf")
write.table(p_an_pvalues, file='richness_inoculation_pval.tsv', quote=FALSE, sep='\t')
write.table(p$data, file='richness_inoculation_data.tsv', quote=FALSE, sep='\t')
p1 <- plot_richness(physeq, x="Treatment", color="Treatment", measures=c("Chao1", "Shannon", "Simpson", "InvSimpson"))
p1 <- p1 + geom_boxplot(data = p1$data, aes(color = NULL), alpha = 0.1)
p1
#Blad_richness <- p1 + ggtitle("Richness, Blad")
#Blad_richness
###-----------Heatmaps------------
temp <- phy_m_filter1
tmp <- temp
tmp <- prune_taxa(names(sort(taxa_sums(temp),TRUE)[1:100]), temp)
p <- plot_heatmap(unique_excl, method="NMDS", distance="jaccard", sample.label="Treatment",
sample.order = "Treatment", title="Core Taxa") +
theme(axis.title.y=element_blank(), axis.title.x =element_blank())
print(p)
ggsave(p, file="Heatmap_core.pdf", width = 20, height = 25, units = "cm")
###-----------Microbiomeseq------
#this package holds a lot of promise, but is still in beta, so if you use it, contact the author on how to reference it.
#It implements a ton of statistical tools and returns nice figures, but is a little buggy.
#Some of the commands in this packae requires a transposed matrix, so first, we'll transpose our phyloseq obejct:
# Extract abundance matrix from the phyloseq object
OTU2 <- otu_table(physeq, taxa_are_rows = TRUE)
OTU2 <- t(OTU2)
#Merge new phyloseq object
TAX = as.matrix(tax_table(physeq))
met <- as.data.frame(sample_data(physeq))
pseq1 <- phyloseq(OTU2, TAX)
MBS_physeq<-merge_phyloseq(pseq1, met)
#OTU <- otu_table(MBS_Pch)
#TAX <- tax_table(MBS_Pch)
#met <- sample_data(MBS_Pch)
#write.table(OTU, file='otu_table.tsv', quote=FALSE, sep='\t')
#write.table(TAX, file='tax_table.tsv', quote=FALSE, sep='\t')
#write.table(met, file="sam_data.tsv", quote=FALSE, sep='\t')
#MBS_physeq is now the transposed version of physeq.
rm(OTU2, TAX, met, pseq1)
MBS_bac <- subset_taxa(MBS_physeq, Kingdom=="k_Bacteria")
MBS_arc <- subset_taxa(MBS_physeq, Kingdom=="k_Archaea")
#subset transposed phyloseq object into inoculations
ID<- as.vector(unique(sample_data(MBS_physeq)$Block))
for (i in 1:length(ID)){
temp <- subset_samples(MBS_physeq, Inoculation==ID[i])
temp = prune_taxa(taxa_sums(temp) > 0, temp)
name <- paste("MBS_", ID[i], sep = "")
assign(name, temp)
}
MBS_
### RUN test_functions.R!!!
#Pielou's evenness = Shannon evenness.
p_an <-plot_anova_diversity_pval(MBS_physeq, method = c("simpson", "shannon", "evenness"),grouping_column ="Treatment",pValueCutoff=0.05)
p_an_o <- p_an[[1]]
p_an_pvalues <- p_an[[2]]
p_an_o <- p_an_o + ggtitle("Diversity index + evenness \n * = p<0.05, ** = p<0.01, *** = p<0.001") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
print(p_an_o)
#write.table(p_an_pvalues, file='richness_o_pval.tsv', quote=FALSE, sep='\t')
#write.table(p_an_o$data, file='richness_o_data.tsv', quote=FALSE, sep='\t')
p_an <-plot_anova_diversity_pval(MBS_Pch_Skopobiota, method = c("shannon", "evenness"),grouping_column ="Fungicide",pValueCutoff=0.05)
p_an_pm <- p_an[[1]]
p_an_pvalues <- p_an[[2]]
p_an_pm <- p_an_pm +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_color_manual(values=set_fun)+
ggtitle("Pathogen + Skopobiota")+
theme(plot.title = element_text(hjust = 0.5))
write.table(p_an_pvalues, file='richness_pm_fun_pval.tsv', quote=FALSE, sep='\t')
write.table(p_an_pm$data, file='richness_pm_fun_data.tsv', quote=FALSE, sep='\t')
p1 <- plot_richness(core, x="Treatment", color="Block", measures=c("Shannon", "InvSimpson"))+
theme_bw() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
p1 <- p1 + geom_boxplot(data = p1$data, aes(color = Treatment), alpha = 0.1)
Richness <- p1 + ggtitle("Richness, Pch")
Richness
p_an <-plot_anova_diversity_pval(MBS_Pch, method = c("shannon", "evenness"),grouping_column ="Fungicide",pValueCutoff=0.05)
p_an_p <- p_an[[1]]
p_an_pvalues <- p_an[[2]]
p_an_p <- p_an_p +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_color_manual(values=set_fun)+
ggtitle("Pathogen")+
theme(plot.title = element_text(hjust = 0.5))
write.table(p_an_pvalues, file='richness_p_fun_pval.tsv', quote=FALSE, sep='\t')
write.table(p_an_p$data, file='richness_p_fun_data.tsv', quote=FALSE, sep='\t')
p_an_p
p_an <-plot_anova_diversity_pval(MBS_Skopobiota, method = c("shannon", "evenness"),grouping_column ="Fungicide",pValueCutoff=0.05)
p_an_m <- p_an[[1]]
p_an_pvalues <- p_an[[2]]
p_an_m <- p_an_m + theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_color_manual(values=set_fun)+
ggtitle("Skopobiota")+
theme(plot.title = element_text(hjust = 0.5))
write.table(p_an_pvalues, file='richness_m_fun_pval.tsv', quote=FALSE, sep='\t')
write.table(p_an_m$data, file='richness_m_fun_data.tsv', quote=FALSE, sep='\t')
p_an_m
p_an <-plot_anova_diversity_pval(MBS_No_inoculation, method = c("shannon", "evenness"),grouping_column ="Fungicide",pValueCutoff=0.05)
p_an_n <- p_an[[1]]
p_an_pvalues <- p_an[[2]]
p_an_n <- p_an_n + theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_color_manual(values=set_fun)+
ggtitle("No Inoculation")+
theme(plot.title = element_text(hjust = 0.5))
write.table(p_an_pvalues, file='richness_n_fun_pval.tsv', quote=FALSE, sep='\t')
write.table(p_an_n$data, file='richness_n_fun_data.tsv', quote=FALSE, sep='\t')
p_an_n
#arrange 4 plots in one
p <- ggarrange(p_an_pm,p_an_m,p_an_p,p_an_n,
widths = 1, heights = 1.1,
#labels = c("Pch+Skopobiota", "Skopobiota", "No inoculation", "Pch"),
#label.x = 0.8, label.y = .90, hjust = 0, vjust = 0,
ncol = 2, nrow = 2,
common.legend = TRUE, legend = "bottom")
p
ggsave(p, file="Richness_ino_fun.pdf", width = 30, height = 20, units = "cm")
###--------------------------LCBD---------------------------
# Plot local contribution to beta diversity
temp <- normalise_data(physeq, norm.method = "relative")
temp <- tax_glom(temp, taxrank = "Family")
taxa_names(temp) = make.unique(tax_table(temp)[,5])
#rownames(sample_data(g_sorted)) = sample_data(physeq)[,13]
tmp <- prune_taxa(names(sort(taxa_sums(physeq),TRUE)[1:25]), physeq)
tmp <- normalise_data(physeq, norm.method = "relative")
#NB: Using standard jaccard does not provide LCBD - use ab.jaccard or ab.sorensen to get abundance data for that.
p <- plot_taxa(temp,grouping_column="Type",method="ab.sorensen",number.taxa=21,filename='Relative_abundance_1_20')
p1 <- p + ggtitle("Relative abundance, 20 most common taxa")
print(p)
ggsave(p, file="Relative_abundance_1_20_treatment_ab_all.pdf", width = 30, height = 20, units = "cm")
write.table(p$data, file='lcbd_data_all.tsv', quote=FALSE, sep='\t')
g_sorted_21 <- prune_taxa(names(sort(taxa_sums(temp),TRUE)[21:50]), physeq)
p <- plot_taxa(g_sorted_21,grouping_column="Treatment",method="jaccard",number.taxa=20,filename='Relative_abundance_21_40_ino')
p1 <- p + ggtitle("Relative abundance, 21-40 most common taxa")
print(p1)
ggsave(p1, file="Relative_abundance_21_40_treatment.pdf", width = 30, height = 20, units = "cm")
#---------------Ordination--------------
ord.res <- ordinate(physeq, which_distance = "bray", method="NMDS", grouping_column="Treatment",pvalue.cutoff = 0.05)
p <- plot_ordination(physeq, ord.res, type="samples", color="Type", shape="Block") +
theme_bw()+
scale_color_manual(values=c(set_2)) +
stat_ellipse(aes(group=Treatment),type = "norm")
p <- p + theme(legend.title = element_blank())+
theme(legend.position="bottom")
print(p)
ggsave(p, file="Ordination_bray_nmds_treatment.pdf")
meta_test <- as.data.frame(sample_data(physeq))
ord.res <- ordination(physeq, which_distance = "bray", method = "NMDS", grouping_column = "Treatment", pvalue.cutoff = 0.05)
ord.res.sol <- ord.res$solution
p <- plot_ordisurf(sol = ord.res.sol, meta_table = meta_test, env.variable = 'pH', grouping_column = "Treatment")
print(p)
p1 <- p + facet_wrap(~Block, 3)
print(p1)
ggsave(p, file="Ordination_jaccard_nmds_treatment_fw.pdf")
#------------DeSeq2-------------
#use transposed phyloseq object
##conglomerate tips based on taxonomic level. Can ease computation in larger datasets.
#temp <- taxa_level(MBS_physeq, "Family")
deseq_sig <- differential_abundance(MBS_physeq2, grouping_column = "Treatment",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = "Deseq2_bac_")
#
#
# deseq_sig <- differential_abundance(MBS_No_inoculation, grouping_column = "Fungicide",
# pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = "deseq_noino")
# deseq_sig <- differential_abundance(MBS_Pch, grouping_column = "Fungicide",
# pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = "deseq_pch")
# deseq_sig <- differential_abundance(MBS_Pch_Skopobiota, grouping_column = "Fungicide",
# pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = "deseq_pch_skopo")
# deseq_sig <- differential_abundance(MBS_Skopobiota, grouping_column = "Fungicide",
# pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = "deseq_skopo")
OTU2 <- otu_table(phy_filter1, taxa_are_rows = TRUE)
OTU2 <- t(OTU2)
#Merge new phyloseq object
TAX = as.matrix(tax_table(physeq10))
met <- as.data.frame(sample_data(physeq10))
pseq1 <- phyloseq(OTU2, TAX)
MBS_physeq2<-merge_phyloseq(pseq1, met)
rm(OTU2, TAX, met, pseq1)
minTotRelAbun = 0.1
x = taxa_sums(MBS_physeq2)
keepTaxa = rownames(as.data.frame(which(((x / sum(x))*100) > minTotRelAbun)))
phy3 = prune_taxa(keepTaxa, MBS_physeq2)
phy3 = MBS_physeq2
ID<- as.vector(unique(sample_data(phy3)$Inoculation))
for (i in 1:length(ID)){
temp <- subset_samples(phy3, Inoculation==ID[i])
temp = prune_taxa(taxa_sums(temp) > 0, temp)
name <- paste("MBS_", ID[i], sep = "")
cont <- subset_samples(temp, Fungicide=="Control")
blad <- subset_samples(temp, Fungicide=="Blad")
cus <- subset_samples(temp, Fungicide=="CuS")
sys <- subset_samples(temp, Fungicide=="Systemics")
tmp <- merge_phyloseq(cont,blad)
name2 <- paste(name,"c-b")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
tmp <- merge_phyloseq(cont,cus)
name2 <- paste(name,"c-cu")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
tmp <- merge_phyloseq(cont,sys)
name2 <- paste(name,"c-s")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
tmp <- merge_phyloseq(cus,blad)
name2 <- paste(name,"cu_b")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
tmp <- merge_phyloseq(cus,sys)
name2 <- paste(name,"cu-s")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
tmp <- merge_phyloseq(sys,blad)
name2 <- paste(name,"s-b")
deseq_sig <- differential_abundance(tmp, grouping_column = "Fungicide",
pvalue.threshold = 0.05, lfc.threshold = 0.1, filename = name2)
}
p <- plot_signif(deseq_sig$plotdata, top.taxa = 100)
print(p)
#
# deseq_sig <- differential_abundance(physeq, grouping_column = "Inoculation", output_norm = "log-relative",
# pvalue.threshold = 0.05, lfc.threshold = 0, filename = F)
# p <- plot_signif(deseq_sig$plotdata, top.taxa = 10)
# print(p)
###---------------Co-occurence networks--------------------
#co-occurence network are a nice way to illustrate which OTUs are likely to occur in the same samples,
#and to illustrate patterns.
tmp <- taxa_level(MBS_physeq, "Genus")
co_occrBlad<- co_occurence_network(MBS_physeq, grouping_column = "Fungicide", rhos = 0.35, method="cor",
qval_threshold=0.05, select.condition = "Blad", scale.vertex.size=4, scale.edge.width=10,
plotNetwork=T, plotBetweennessEeigenvalue=T)
co_occrCtrl <- co_occurence_network(tmp, grouping_column = "Fungicide", rhos = 0.35, method="cor",
qval_threshold=0.05, select.condition = "Control", scale.vertex.size=4, scale.edge.width=10,
plotNetwork=T, plotBetweennessEeigenvalue=T)
co_occrCuS <- co_occurence_network(tmp, grouping_column = "Fungicide", rhos = 0.35, method="cor",
qval_threshold=0.05, select.condition = "CuS", scale.vertex.size=4, scale.edge.width=10,
plotNetwork=T, plotBetweennessEeigenvalue=T)
co_occrSys <- co_occurence_network(tmp, grouping_column = "Fungicide", rhos = 0.35, method="cor",
qval_threshold=0.05, select.condition = "Systemics", scale.vertex.size=4, scale.edge.width=10,
plotNetwork=T, plotBetweennessEeigenvalue=T)
require(visNetwork)
g <- co_occrBlad$net$graph
h <- co_occrCtrl$net$graph
i <- co_occrCuS$net$graph
j <- co_occrSys$net$graph
data <- toVisNetworkData(g)
visNetwork(nodes = data$nodes, edges = data$edges, main="physeq dataset, Blad. ", width = 900)%>%
visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE)
data <- toVisNetworkData(h)
visNetwork(nodes = data$nodes, edges = data$edges, main="physeq dataset, Ctrl. ", width = 900)%>%
visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE)
data <- toVisNetworkData(i)
visNetwork(nodes = data$nodes, edges = data$edges, main="physeq dataset, CuS. ", width = 900)%>%
visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE)
data <- toVisNetworkData(j)
visNetwork(nodes = data$nodes, edges = data$edges, main="physeq dataset, Sys. ", width = 900)%>%
visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE)
###-----------Metacoder----------
#Metacoder provides heat trees, which colour the branches of the tree that
# has any significant change in abundance between two treatments.
temp <- normalise_data(core, norm.method = "relative")
#remember to clear cache (i.e. delete obj before doingn this, otherwise it messes up sometimes. )
temp <- tax_glom(subset_lefse, taxrank = "Family")
#temp <- physeq
subset_lefse
temp1 <- subset_taxa(temp, Kingdom=="k_Bacteria")
temp1 = prune_taxa(taxa_sums(temp1) > 0, temp1)
temp2 <- subset_taxa(temp, Kingdom=="k_Archaea")
temp2 = prune_taxa(taxa_sums(temp2) > 0, temp2)
#parse phyloseq object physeq
obj <- parse_phyloseq(temp1)
tissuegroup <- obj$data$sample_data$Treatment
# Convert counts to proportions
obj$data$otu_table <- calc_obs_props(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
# Calculate per-taxon proportions
obj$data$tax_abund <- calc_taxon_abund(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
#Calculate per-type occurence
obj$data$tax_occ <- calc_n_samples(obj, "tax_abund", groups = tissuegroup)
#Calculate difference between treatments
obj$data$diff_table <- compare_groups(obj, data = "tax_abund", cols = obj$data$sample_data$sample_id, groups = tissuegroup)
heat_tree(obj,
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_trans = "linear",
node_color_interval = c(-3, 3),
edge_color_interval = c(-3, 3),
layout = "davidson-harel",
initial_layout = "reingold-tilford",
node_color_range = c("red4", "red", "gray95", "royalblue", "darkblue"),
#title = "Blue = Upregulated in Control \n Red = Upregulated in Exclosures",
#title_size = 0.05,
node_size_axis_label = "Number of species",
node_color_axis_label = "Log2 ratio median proportions",
output_file = 'heattree_treatment_signlefse_f.pdf')
test <- obj$data$diff_table %>%
mutate(taxon_names = taxon_names(obj)[taxon_id],
taxon_ranks = taxon_ranks(obj)[taxon_id]) %>%
arrange(desc(abs(obj$data$diff_table$log2_median_ratio)))
write.table(test, file='heattree_treatment.tsv', quote=FALSE, sep='\t')
#Test p-values
obj$data$diff_table$wilcox_p_value <- p.adjust(obj$data$diff_table$wilcox_p_value, method = "fdr")
test <- within(obj$data$diff_table, log2_median_ratio[wilcox_p_value > 0.05] <- 0 )
obj$data$diff_table <- test
heat_tree(obj,
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_trans = "linear",
node_color_interval = c(-3, 3),
edge_color_interval = c(-3, 3),
layout = "davidson-harel",
initial_layout = "reingold-tilford",
node_color_range = c("red4", "red", "gray95", "royalblue", "darkblue"),
#title = "Heat Tree Matrix",
title_size = 0.05,
node_size_axis_label = "Number of species",
node_color_axis_label = "Log2 ratio median proportions",
output_file = 'heattree_treatment.pdf')
###--------------------heat tree matrices----------------------------
heat_tree_matrix(obj,
data = "diff_table",
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_trans = "linear",
node_color_interval = c(-3, 3),
edge_color_interval = c(-3, 3),
layout = "davidson-harel",
initial_layout = "reingold-tilford",
node_color_range = c("darkblue", "royalblue", "gray95", "red", "red4"),
#title = "Heat Tree Matrix",
title_size = 0.05,
node_size_axis_label = "Number of species",
node_color_axis_label = "Log2 ratio median proportions",
output_file = 'heattree_treatment_pval.pdf')
test <- obj$data$diff_table %>%
mutate(taxon_names = taxon_names(obj)[taxon_id],
taxon_ranks = taxon_ranks(obj)[taxon_id]) %>%
arrange(desc(abs(obj$data$diff_table$log2_median_ratio)))
write.table(test, file='heattree_treatment_pval.tsv', quote=FALSE, sep='\t')
### Control
tissuegroup <- obj$data$sample_data$Control
# Convert counts to proportions
obj$data$otu_table <- calc_obs_props(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
# Calculate per-taxon proportions
obj$data$tax_abund <- calc_taxon_abund(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
#Calculate per-type occurence
obj$data$tax_occ <- calc_n_samples(obj, "tax_abund", groups = tissuegroup)
#Calculate difference between treatments
obj$data$diff_table <- compare_groups(obj, data = "tax_abund", cols = obj$data$sample_data$sample_id, groups = tissuegroup)
# heat_tree_matrix(obj,
# data = "diff_table",
# node_size = n_obs,
# node_label = taxon_names,
# node_color = log2_median_ratio,
# node_color_trans = "linear",
# node_color_interval = c(-3, 3),
# edge_color_interval = c(-3, 3),
# layout = "davidson-harel",
# initial_layout = "reingold-tilford",
# node_color_range = c("darkblue", "royalblue", "gray95", "red", "red4"),
# #title = "Heat Tree Matrix",
# title_size = 0.05,
# node_size_axis_label = "Number of species",
# node_color_axis_label = "Log2 ratio median proportions",
# output_file = 'heattree_treatment.pdf')
heat_tree(obj,
node_label = taxon_names,
node_size = n_obs,
node_color = log2_median_ratio,
node_color_interval = c(-2, 2),
node_color_range = c("darkblue", "royalblue", "gray95", "red", "red4"),
node_size_axis_label = "species count",
node_color_axis_label = "Log 2 ratio of median proportions",
layout = "davidson-harel",
initial_layout = "reingold-tilford",
title = "Red Nodes = Higher Abundance in Control, No Inoculation \n Blue Nodes = Higher Abundance in Treatment",
title_size = 0.035,
edge_label_size_range = c(1,5),
output_file = 'heattree_control.pdf')
test <- obj$data$diff_table %>%
mutate(taxon_names = taxon_names(obj)[taxon_id],
taxon_ranks = taxon_ranks(obj)[taxon_id]) %>%
arrange(desc(abs(obj$data$diff_table$log2_median_ratio)))
write.table(test, file='heattree_treatment.tsv', quote=FALSE, sep='\t')
#Test p-values
obj$data$diff_table$wilcox_p_value <- p.adjust(obj$data$diff_table$wilcox_p_value, method = "fdr")
test <- within(obj$data$diff_table, log2_median_ratio[wilcox_p_value > 0.05] <- 0 )
obj$data$diff_table <- test
heat_tree(obj,
node_label = taxon_names,
node_size = n_obs,
node_color = log2_median_ratio,
node_color_interval = c(-2, 2),
node_color_range = c("darkblue", "royalblue", "gray95", "red", "red4"),
node_size_axis_label = "species count",
node_color_axis_label = "Log 2 ratio of median proportions",
layout = "davidson-harel",
initial_layout = "reingold-tilford",
title = "Red Nodes = Significantly Higher Abundance in Control, No Inoculation \n Blue Nodes = Significantly Higher Abundance in Treatment",
title_size = 0.035,
edge_label_size_range = c(1,5),
output_file = 'heattree_control_pval.pdf')
test <- obj$data$diff_table %>%
mutate(taxon_names = taxon_names(obj)[taxon_id],
taxon_ranks = taxon_ranks(obj)[taxon_id]) %>%
arrange(desc(abs(obj$data$diff_table$log2_median_ratio)))
write.table(test, file='heattree_treatment_pval.tsv', quote=FALSE, sep='\t')
### Inoculaton
tissuegroup <- obj$data$sample_data$Inoculation
# Convert counts to proportions
obj$data$otu_table <- calc_obs_props(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
# Calculate per-taxon proportions
obj$data$tax_abund <- calc_taxon_abund(obj, data = "otu_table", cols = obj$data$sample_data$sample_id)
#Calculate per-type occurence
obj$data$tax_occ <- calc_n_samples(obj, "tax_abund", groups = tissuegroup)
#Calculate difference between treatments
obj$data$diff_table <- compare_groups(obj, data = "tax_abund", cols = obj$data$sample_data$sample_id, groups = tissuegroup)
heat_tree_matrix(obj,
data = "diff_table",
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_trans = "linear",