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16S_analysis_ASVs_all_BUT_differentialAbundance_script_v46.RMD
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16S_analysis_ASVs_all_BUT_differentialAbundance_script_v46.RMD
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---
title: "Azteca-Cecropia 16S rRNA gene manuscript - ASV level"
author: "Maximilian Nepel"
date: "last updated August 2023"
output:
html_document:
toc: true
toc_float: yes
comment: recently switched from OTUs to DADA2-inferred ASVs. Analysis scripts had been modified, but otu/OTU syntax was not replaced with asv/ASV.
---
```{r, warning=FALSE, message=FALSE}
source("0_v01_common.R", chdir = TRUE) # runs the common.R file
```
# loaded libraries
```{r}
all.pkgs.loaded <- gsub("package:","", search()[grep("package:",search())])
sessionInfo()$basePkgs
pkgs.loaded <- all.pkgs.loaded[!all.pkgs.loaded %in% sessionInfo()$basePkgs]
pkgs <- data.frame(matrix(ncol = 1,nrow = 0))
colnames(pkgs) <- c("Version")
for (i in pkgs.loaded) {
pkgs[i,1] <- sessionInfo()$otherPkgs[[i]]$Version
}
print(pkgs)
```
# all subsetted phyloseq datasets are saved as .RDS files and need to be loaded here
```{r}
## examples
# physeq.Ep.mean.abs <- readRDS(file.path(RD_DIR, "ASVs.physeq.Ep.mean.abs.rds"))
# physeq.p.norm <- readRDS(file.path(RD_DIR, "ASVs.physeq.p.norm.rds"))
```
## first glimpse on taxonomy
```{r}
(physeq4norm.ansys <- physeq.p.norm)
# no. of OTUs
nrow(tax_table(physeq4norm.ansys))
# detected phyla
length(unique(tax_table(physeq4norm.ansys)[,"Phylum"]))
levels(as.factor(tax_table(physeq4norm.ansys)[,"Phylum"]))
```
# ###########################################################################
# #################### analyses #############################################
# ###########################################################################
## 1. describe Fp = intial ant colony patches (IP) ###########################################
```{r}
(physeq4abs.ansys <- physeq.Fp.abs)
(physeq4abs.ansys <- prune_taxa(taxa_sums(physeq4abs.ansys) > 0, physeq4abs.ansys))
(physeq4norm.ansys <- physeq.Fp.norm)
(physeq4norm.ansys <- prune_taxa(taxa_sums(physeq4norm.ansys) > 0, physeq4norm.ansys))
plottitle <- "ASV.ds.Fp"
table(get_variable(physeq4norm.ansys)$Ant)
sum(table(get_variable(physeq4norm.ansys)$Ant))
unique(paste0(get_variable(physeq4norm.ansys)$Tree))
```
### 1.1 alpha diversity- shannon
```{r}
plot_richness(physeq4abs.ansys, measures = c("Shannon"))
### testing patch type alpha diversity for significance
alpha <- get_variable(physeq4abs.ansys)
alpha$Shannon <- estimate_richness(physeq4abs.ansys, measures = c("Shannon"))$Shannon
(alpha.res <- kruskal.test(Shannon ~ Ant, data = alpha))
(alpha.res.pw <- pairwise.wilcox.test(alpha$Shannon, alpha$Ant, p.adjust.method = "BH"))
p <- ggplot(alpha, aes(Ant, Shannon))
p + geom_boxplot(aes(fill = Ant), outlier.colour="red", outlier.shape=NA, outlier.size=2) +
ggtitle(plottitle) +
coord_cartesian(ylim = c(0.0, 6)) +
geom_jitter(color="darkblue", shape=16, position=position_jitter(0.2)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
ggsave(paste(PLOTS_DIR,"/alpha.boxplot.Fp.dots",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 5, dpi = 300)
alpha.sub <- alpha[grep("alf", alpha$Ant),]
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("con", alpha$Ant),]
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("xan", alpha$Ant),]
median(alpha.sub$Shannon)
```
### 1.2 beta diversity - PCoA
```{r}
bplot.obj <- capscale(otu_table(physeq4norm.ansys) ~ 1, distance = "bray")
bplot.scores <- scores(bplot.obj)
explained <- eigenvals( bplot.obj )/sum( eigenvals( bplot.obj ) ) * 100
explained <- format( round( explained, 1 ), nsmall = 1 )
bplot.df <- data.frame(id = row.names(bplot.scores$sites), bplot.scores$sites, Name = get_variable(physeq4norm.ansys)$Name, Type = get_variable(physeq4norm.ansys)$Type, Year = get_variable(physeq4norm.ansys)$Year, Ant = get_variable(physeq4norm.ansys)$Ant, Age = get_variable(physeq4norm.ansys)$Age, Tree = get_variable(physeq4norm.ansys)$TreeR)
colnames(bplot.df)[c(2,3)] <- c("PC1", "PC2")
p <- PlotOrd.single(bplot.df , components = c("PC1", "PC2"), groups = "Ant", variance = TRUE, connect = FALSE)
p + scale_size_manual(values = c(4, 8)) + ggtitle(plottitle) +
xlab(paste("PC 1 (", explained[1], "%)", sep = "")) +
ylab(paste("PC 2 (", explained[2], "%)", sep = ""))
ggsave(paste(PLOTS_DIR,"/plot.PCoA.Fp.Ant",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 4, dpi = 300)
```
### 1.3 drivers
#### 1.3.1 PERMANOVA - adonis2
```{r}
(m.Fp.Ant <- adonis2(otu_table(physeq4norm.ansys) ~ Ant,
data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
```
### 1.4 community - bar charts
#### 1.4.1 base for stacked bar charts
```{r}
OTU4tax <- as.data.frame(t(otu_table(physeq4norm.ansys)))
OTU4tax$Total <- rowSums(OTU4tax)
OTU4tax$OTU <- rownames(OTU4tax)
dim(OTU4tax)
Taxonomy2 <- cbind(physeq4norm.ansys@tax_table@.Data, OTU4tax)
Taxonomy2[,1:6] <- lapply(Taxonomy2[,1:6], as.factor)
Taxonomy2 <- melt(Taxonomy2, id = c("OTU", "Total", "Domain", "Phylum", "Class", "Order", "Family", "Genus"), variable.name = 'Name', value.name = 'Freq', na.rm = TRUE) # --> jede OTU in jedem sample einen eintrag mit frequenz
Taxonomy <- merge(Taxonomy2, get_variable(physeq4norm.ansys), by="Name")
ab.Taxonomy.class <- TaxThresh(Taxonomy, tax.level = "Class", thresh = 0.5)
ab.Taxonomy.order <- TaxThresh(Taxonomy, tax.level = "Order", thresh = 0.2)
```
#### 1.4.2 class taxonomy
##### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.class
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Fp.class",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 4, dpi = 300)
```
##### get statistics
```{r}
head(unique(Taxonomy$Class))
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
sum.class.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.class.taxSample)
# get statistics min mean max
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
d6[k,3] <- round(mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,4] <- max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
}
print(d6[order(d6[,3],decreasing = TRUE),])
```
#### 1.4.3 order taxonomy
##### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.order
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Order, Class = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Fp.order",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 7, height = 6, dpi = 300)
```
##### get statistics
```{r}
head(unique(Taxonomy$Order))
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
sum.class.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.class.taxSample)
# get statistics min mean max
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- round(min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,3] <- round(mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,4] <- round(max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
}
print(d6[order(d6[,3],decreasing = TRUE),])
```
### 1.5 core community - rel. ab.
```{r}
(physeq.core <- physeq4norm.ansys)
## ASV level
{prev <- 50 # min % of samples
det <- 0.4 # min % per sample
(sub.core <- core(physeq.core, detection = det, prevalence = prev/100))
print(sub.core)}
tax.core <- as.data.frame(tax_table(sub.core))
tax.core$taxa <- row.names(tax.core)
# calculate average relative abundance of abundant taxa
tax.core$av.ab <- colSums(otu_table(physeq.core)[,colnames(otu_table(physeq.core)) %in% tax.core$taxa])/nrow(otu_table(physeq.core))
print(tax.core)
unique(tax.core$Class)
sum(tax.core$av.ab) # abundant taxa account for X% of total reads (based on rel.ab. per sample)
tax.core.sort <- tax.core[order(tax.core$Phylum, tax.core$Class, tax.core$Order, tax.core$Family, tax.core$Genus),]
print(tax.core.sort)
```
## 2. describe YCp = young ant colony patches (YP) ###########################################
```{r}
(physeq4abs.ansys <- physeq.YCp.abs)
(physeq4abs.ansys <- prune_taxa(taxa_sums(physeq4abs.ansys) > 0, physeq4abs.ansys))
(physeq4norm.ansys <- physeq.YCp.norm)
(physeq4norm.ansys <- prune_taxa(taxa_sums(physeq4norm.ansys) > 0, physeq4norm.ansys))
plottitle <- "ASV.ds.Ycp.norm"
table(get_variable(physeq4norm.ansys)$Ant)
sum(table(get_variable(physeq4norm.ansys)$Ant))
length(unique(paste0(get_variable(physeq4norm.ansys)$Tree)))
```
### 2.1 alpha diversity - shannon
```{r}
plot_richness(physeq4abs.ansys, measures = c("Shannon"))
### testing patch type alpha diversity for significance
alpha <- get_variable(physeq4abs.ansys)
alpha$Shannon <- estimate_richness(physeq4abs.ansys, measures = c("Shannon"))$Shannon
(alpha.res <- kruskal.test(Shannon ~ Ant, data = alpha))
(alpha.res.pw <- pairwise.wilcox.test(alpha$Shannon, alpha$Ant, p.adjust.method = "BH"))
p <- ggplot(alpha, aes(Ant, Shannon))
p + geom_boxplot(aes(fill = Ant), outlier.colour="red", outlier.shape=NA, outlier.size=2) +
ggtitle(plottitle) +
coord_cartesian(ylim = c(0.0, 6)) +
geom_jitter(color="darkblue", shape=16, position=position_jitter(0.2)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
ggsave(paste(PLOTS_DIR,"/alpha.boxplot.YCp.dots",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 4, height = 5, dpi = 300)
alpha.sub <- alpha[grep("alf", alpha$Ant),]
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("con", alpha$Ant),]
median(alpha.sub$Shannon)
```
### 2.2 beta diversity - PCoA
```{r}
bplot.obj <- capscale(otu_table(physeq4norm.ansys) ~ 1, distance = "bray")
bplot.scores <- scores(bplot.obj)
explained <- eigenvals( bplot.obj )/sum( eigenvals( bplot.obj ) ) * 100
explained <- format( round( explained, 1 ), nsmall = 1 )
bplot.df <- data.frame(id = row.names(bplot.scores$sites), bplot.scores$sites, Name = get_variable(physeq4norm.ansys)$Name, Type = get_variable(physeq4norm.ansys)$Type, Year = get_variable(physeq4norm.ansys)$Year, Ant = get_variable(physeq4norm.ansys)$Ant, Age = get_variable(physeq4norm.ansys)$Age, Tree = get_variable(physeq4norm.ansys)$TreeR)
colnames(bplot.df)[c(2,3)] <- c("PC1", "PC2")
# Ant
p <- PlotOrd.single(bplot.df , components = c("PC1", "PC2"), groups = "Ant", variance = TRUE, connect = FALSE)
p + scale_size_manual(values = c(4, 8)) + ggtitle(plottitle) +
xlab(paste("PC 1 (", explained[1], "%)", sep = "")) +
ylab(paste("PC 2 (", explained[2], "%)", sep = ""))
ggsave(paste(PLOTS_DIR,"/plot.PCoA.YC.Ant",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 4, dpi = 300)
```
### 2.3 drivers
#### 2.3.1 PERMANOVA - adonis2
```{r}
(m.YC.Ant <- adonis2(otu_table(physeq4norm.ansys) ~ Ant, data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
(m.YC.Tree <- adonis2(otu_table(physeq4norm.ansys) ~ TreeR, data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
```
### 2.4 community - bar charts
#### 2.4.1 base for stacked bar charts
```{r}
OTU4tax <- as.data.frame(t(otu_table(physeq4norm.ansys)))
OTU4tax$Total <- rowSums(OTU4tax)
OTU4tax$OTU <- rownames(OTU4tax)
dim(OTU4tax)
Taxonomy2 <- cbind(physeq4norm.ansys@tax_table@.Data, OTU4tax) # combine data.frames
Taxonomy2[,1:6] <- lapply(Taxonomy2[,1:6], as.factor)
Taxonomy2 <- melt(Taxonomy2, id = c("OTU", "Total", "Domain", "Phylum", "Class", "Order", "Family", "Genus"), variable.name = 'Name', value.name = 'Freq', na.rm = TRUE) # --> jede OTU in jedem sample einen eintrag mit frequenz
Taxonomy <- merge(Taxonomy2, get_variable(physeq4norm.ansys), by="Name")
ab.Taxonomy.phylum <- TaxThresh(Taxonomy, tax.level = "Phylum", thresh = 0.0)
ab.Taxonomy.class <- TaxThresh(Taxonomy, tax.level = "Class", thresh = 0.5) # get ONLY abundant taxonomy
ab.Taxonomy.order <- TaxThresh(Taxonomy, tax.level = "Order", thresh = 0.2)
```
#### 2.4.2 class taxonomy
##### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.class
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Ant == tax.data2plot$Ant[i]])
tax.data2plot$Ant.Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.YC.class",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 7, height = 5, dpi = 300)
```
##### get statistics
```{r}
head(unique(Taxonomy$Class))
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
sum.class.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.class.taxSample)
# get statistics min mean max
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- round(min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,3] <- round(mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,4] <- round(max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
}
print(d6[order(d6[,"mean"],decreasing = TRUE),])
```
#### 2.4.3 order taxonomy
##### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.order
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Order, Class = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Ant == tax.data2plot$Ant[i]])
tax.data2plot$Ant.Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.YC.order",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 7, height = 5, dpi = 300)
```
##### get statistics
```{r}
head(unique(Taxonomy$Order))
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
sum.class.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.class.taxSample)
# get statistics min mean max
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- round(min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,3] <- round(mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
d6[k,4] <- round(max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]),1)
}
print(d6[order(d6[,"mean"],decreasing = TRUE),])
```
### 2.4 core community - rel. ab.
```{r}
(physeq.core <- physeq4norm.ansys)
## ASV >?% in >?% of samples
{prev <- 50 # min % of samples
det <- 0.4 # min % per sample
(sub.core <- core(physeq.core, detection = det, prevalence = prev/100))
print(sub.core)}
tax.core <- as.data.frame(tax_table(sub.core))
tax.core$taxa <- row.names(tax.core)
# calculate average relative abundance of abundant taxa
tax.core$av.ab <- colSums(otu_table(physeq.core)[,colnames(otu_table(physeq.core)) %in% tax.core$taxa])/nrow(otu_table(physeq.core))
print(tax.core)
unique(tax.core$Class)
sum(tax.core$av.ab) # abundant taxa account for X% of total reads (based on rel.ab. per sample)
tax.core.sort <- tax.core[order(tax.core$Phylum, tax.core$Class, tax.core$Order, tax.core$Family, tax.core$Genus),]
print(tax.core.sort)
```
## 3. describe Ep = established ant colony patches (EP) ######################################
### 3.1 variation within ant colonies - age (only trees > 1 sample)
```{r}
(physeq4abs.ansys <- physeq.Ep.age.abs)
(physeq4abs.ansys <- prune_taxa(taxa_sums(physeq4abs.ansys) > 0, physeq4abs.ansys))
(physeq4norm.ansys <- physeq.Ep.age.norm)
(physeq4norm.ansys <- prune_taxa(taxa_sums(physeq4norm.ansys) > 0, physeq4norm.ansys))
plottitle <- "ASV.ds.Ep.age"
table(get_variable(physeq4norm.ansys)$Ant)
unique(paste0(get_variable(physeq4norm.ansys)$Ant,".", get_variable(physeq4norm.ansys)$Tree))
```
#### 3.1.1 alpha diversity - shannon
```{r}
plot_richness(physeq4abs.ansys, measures = c("Shannon"))
### testing patch type alpha diversity for significance
alpha <- get_variable(physeq4abs.ansys)
alpha$Shannon <- estimate_richness(physeq4abs.ansys, measures = c("Shannon"))$Shannon
(alpha.res <- kruskal.test(Shannon ~ Age, data = alpha))
(alpha.res.pw <- pairwise.wilcox.test(alpha$Shannon, alpha$Age, p.adjust.method = "BH"))
alpha.sub <- alpha[grep("\\.I\\.", alpha$Name),]
mean(alpha.sub$Shannon)
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("\\.II\\.", alpha$Name),]
mean(alpha.sub$Shannon)
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("\\.III\\.", alpha$Name),]
mean(alpha.sub$Shannon)
median(alpha.sub$Shannon)
p <- ggplot(alpha, aes(Age, Shannon))
p + geom_boxplot(aes(fill = Age), outlier.colour="red", outlier.shape=NA, outlier.size=2) +
ggtitle(plottitle) +
coord_cartesian(ylim = c(0.0, 6)) +
geom_jitter(color="darkblue", shape=16, position=position_jitter(0.2)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
ggsave(paste(PLOTS_DIR,"/alpha.boxplot.EpAge.dots",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 5, dpi = 300)
p + geom_boxplot(aes(fill = Age), outlier.colour="red", outlier.shape=NA, outlier.size=2) +
ggtitle(plottitle) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
ggsave(paste(PLOTS_DIR,"/alpha.boxplot.EpAge",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 5, dpi = 300)
```
#### 3.1.2 beta diversity - PCoA
```{r}
bplot.obj <- capscale(otu_table(physeq4norm.ansys) ~ 1, distance = "bray")
bplot.scores <- scores(bplot.obj)
explained <- eigenvals( bplot.obj )/sum( eigenvals( bplot.obj ) ) * 100
explained <- format( round( explained, 1 ), nsmall = 1 )
bplot.df <- data.frame(id = row.names(bplot.scores$sites), bplot.scores$sites, Name = get_variable(physeq4norm.ansys)$Name, Type = get_variable(physeq4norm.ansys)$Type, Year = get_variable(physeq4norm.ansys)$Year, Ant = get_variable(physeq4norm.ansys)$Ant, Age = get_variable(physeq4norm.ansys)$Age, Tree = get_variable(physeq4norm.ansys)$TreeR)
colnames(bplot.df)[c(2,3)] <- c("PC1", "PC2")
# Age
p <- PlotOrd.single(bplot.df , components = c("PC1", "PC2"), groups = "Age", variance = TRUE, connect = FALSE)
p + scale_size_manual(values = c(4, 8)) + ggtitle(plottitle) +
xlab(paste("PC 1 (", explained[1], "%)", sep = "")) +
ylab(paste("PC 2 (", explained[2], "%)", sep = ""))
```
#### 3.1.3 drivers
##### 3.1.3 PERMANOVA - adonis2
```{r}
(m.EpAge.Age <- adonis2(otu_table(physeq4norm.ansys) ~ Age,
data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
(m.EpAge.Tree <- adonis2(otu_table(physeq4norm.ansys) ~ TreeR,
data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
(m.EpAge.AntStrat.Age <- adonis2(otu_table(physeq4norm.ansys) ~ Age,
strata = get_variable(physeq4norm.ansys)$Ant, data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
```
##### plotting - dbRDA
```{r}
bplot.obj <- capscale(otu_table(physeq4norm.ansys) ~ TreeR, data=get_variable(physeq4norm.ansys), distance = "bray")
bplot.scores <- scores(bplot.obj)
explained <- eigenvals( bplot.obj )/sum( eigenvals( bplot.obj ) ) * 100
explained <- format( round( explained, 1 ), nsmall = 1 )
bplot.df <- data.frame(id = row.names(bplot.scores$sites), bplot.scores$sites, Name = get_variable(physeq4norm.ansys)$Name, Type = get_variable(physeq4norm.ansys)$Type, Year = get_variable(physeq4norm.ansys)$Year, Ant = get_variable(physeq4norm.ansys)$Ant, Age = get_variable(physeq4norm.ansys)$Age, Tree = get_variable(physeq4norm.ansys)$TreeR)
colnames(bplot.df)[c(2,3)] <- c("PC1", "PC2")
p <- PlotOrd.single.bP(bplot.df , components = c("PC1", "PC2"), groups = "Tree", variance = TRUE, connect = FALSE)
p + scale_size_manual(values = c(4, 8)) + ggtitle(plottitle) +
xlab(paste("dbRDA 1 (", explained[1], "%)", sep = "")) +
ylab(paste("dbRDA 2 (", explained[2], "%)", sep = ""))
ggsave(paste(PLOTS_DIR,"/plot.dbRDA.EpAge.Tree",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5.6, height = 4, dpi = 300)
```
### 3.2 Ep averaged trees - one sample per establ. ant colony
```{r}
(physeq4abs.ansys <- physeq.Ep.mean.abs)
(physeq4abs.ansys <- prune_taxa(taxa_sums(physeq4abs.ansys) > 0, physeq4abs.ansys))
(physeq4norm.ansys <- physeq.Ep.mean.norm)
(physeq4norm.ansys <- prune_taxa(taxa_sums(physeq4norm.ansys) > 0, physeq4norm.ansys))
plottitle <- "ASV.new.ds.Ep.mean-RNAlater.only"
table(get_variable(physeq4norm.ansys)$Ant)
unique(paste0(get_variable(physeq4norm.ansys)$Ant,".", get_variable(physeq4norm.ansys)$Tree))
```
#### 3.2.1 alpha diversity - shannon
```{r}
plot_richness(physeq4abs.ansys, measures = c("Shannon"))
### testing patch type alpha diversity for significance
alpha <- get_variable(physeq4abs.ansys)
alpha$Shannon <- estimate_richness(physeq4abs.ansys, measures = c("Shannon"))$Shannon
(alpha.res <- wilcox.test(Shannon ~ Ant, data = alpha))
p <- ggplot(alpha, aes(Ant, Shannon))
p + geom_boxplot(aes(fill = Ant), outlier.colour="red", outlier.shape=NA, outlier.size=2) +
ggtitle(plottitle) +
coord_cartesian(ylim = c(0.0, 6)) +
geom_jitter(color="darkblue", shape=16, position=position_jitter(0.2)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
ggsave(paste(PLOTS_DIR,"/alpha.boxplot.Epmean.dots",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 4, height = 5, dpi = 300)
alpha.sub <- alpha[grep("alf", alpha$Ant),]
median(alpha.sub$Shannon)
alpha.sub <- alpha[grep("con", alpha$Ant),]
median(alpha.sub$Shannon)
```
#### 3.2.2 drivers - Ep.mean
##### PERMANOVA - adonis2
```{r}
(m.Epmean.Ant <- adonis2(otu_table(physeq4norm.ansys) ~ Ant,
data = get_variable(physeq4norm.ansys), method = "bray", permutations = 9999))
```
##### plotting - dbRDA
```{r}
# Ant
bplot.obj <- capscale(otu_table(physeq4norm.ansys) ~ Ant, data=get_variable(physeq4norm.ansys), distance = "bray")
bplot.scores <- scores(bplot.obj)
explained <- eigenvals( bplot.obj )/sum( eigenvals( bplot.obj ) ) * 100
explained <- format( round( explained, 1 ), nsmall = 1 )
bplot.df <- data.frame(id = row.names(bplot.scores$sites), bplot.scores$sites, Name = get_variable(physeq4norm.ansys)$Name, Type = get_variable(physeq4norm.ansys)$Type, Year = get_variable(physeq4norm.ansys)$Year, Ant = get_variable(physeq4norm.ansys)$Ant, Age = get_variable(physeq4norm.ansys)$Age, Tree = get_variable(physeq4norm.ansys)$TreeR)
colnames(bplot.df)[c(2,3)] <- c("PC1", "PC2")
p <- PlotOrd.single(bplot.df , components = c("PC1", "PC2"), groups = "Ant", variance = TRUE, connect = FALSE)
p + scale_size_manual(values = c(4, 8)) + ggtitle(plottitle) +
xlab(paste("dbRDA 1 (", explained[1], "%)", sep = "")) +
ylab(paste("dbRDA 2 (", explained[2], "%)", sep = ""))
ggsave(paste(PLOTS_DIR,"/plot.dbRDA.Epmean.Ant",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 5, height = 4, dpi = 300)
```
#### 3.2.3 community - bar charts
##### base for stacked bar charts
```{r}
OTU4tax <- as.data.frame(t(otu_table(physeq4norm.ansys)))
OTU4tax$Total <- rowSums(OTU4tax)
OTU4tax$OTU <- rownames(OTU4tax)
dim(OTU4tax)
Taxonomy2 <- cbind(physeq4norm.ansys@tax_table@.Data, OTU4tax)
Taxonomy2[,1:6] <- lapply(Taxonomy2[,1:6], as.factor)
Taxonomy2 <- melt(Taxonomy2, id = c("OTU", "Total", "Domain", "Phylum", "Class", "Order", "Family", "Genus"), variable.name = 'Name', value.name = 'Freq', na.rm = TRUE)
Taxonomy <- merge(Taxonomy2, get_variable(physeq4norm.ansys), by="Name")
ab.Taxonomy.domain <- TaxThresh(Taxonomy, tax.level = "Domain", thresh = 0.0)
ab.Taxonomy.phylum <- TaxThresh(Taxonomy, tax.level = "Phylum", thresh = 0.0)
ab.Taxonomy.class <- TaxThresh(Taxonomy, tax.level = "Class", thresh = 0.5)
ab.Taxonomy.order <- TaxThresh(Taxonomy, tax.level = "Order", thresh = 0.5)
```
##### class taxonomy
###### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.class
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Ant == tax.data2plot$Ant[i]])
tax.data2plot$Ant.Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Epmean.class",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 7, height = 5, dpi = 300)
## plot stacked bar chart per ant
AllAnt.bar <- qplot(x=Ant,data=tax.data2plot,geom="bar",weight=Ant.Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllAnt.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Epmean.class.Ant",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 7, height = 5, dpi = 300)
```
###### get statistics
```{r}
head(unique(Taxonomy$Phylum))
head(unique(Taxonomy$Class))
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
sum.class.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.class.taxSample)
# get statistics min mean max
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
d6[k,3] <- mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
d6[k,4] <- max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
}
print(d6[order(d6[,"mean"],decreasing = TRUE),])
# Ant (Ep) variation
tax.data2plot$taxAnt <- paste(tax.data2plot$Taxa, tax.data2plot$Ant, sep="_")
sum.class.taxAnt <- aggregate(tax.data2plot$Ant.Rel.Ab, by=list(taxYZ=tax.data2plot$taxAnt), FUN=sum)
head(sum.class.taxAnt)
sum(sum.class.taxAnt$x)
# get statistics per ant species
sum.class2check <- sum.class.taxAnt # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d7 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d7) <- c("Taxa","alf","con", "mean") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d7[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d7[k,2:3] <- sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]]
d7[k,4] <- mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
}
print(d7[order(d7[,"mean"],decreasing = TRUE),])
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
# get statistics min mean max per ant species
for (j in c("alf", "con")){
tax.data2plot.ant <- tax.data2plot[tax.data2plot$Ant == j,]
sum.class.taxSample <- aggregate(tax.data2plot.ant$Rel.Ab, by=list(taxYZ=tax.data2plot.ant$taxSample), FUN=sum)
head(sum.class.taxSample)
sum.class2check <- sum.class.taxSample # sum.class.taxType
split <- str_split_fixed(sum.class2check$taxYZ, "_", 2)
sum.class.min.max.2 <- cbind(split,sum.class2check)
colnames(sum.class.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.class.min.max.2["Taxa"] <- lapply(sum.class.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c(paste0(j,".Taxa"),"min","mean", "max") # per site
for (k in 1:length(levels(sum.class.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.class.min.max.2$Taxa)[[k]]
d6[k,2] <- min(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
d6[k,3] <- mean(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
d6[k,4] <- max(sum.class.min.max.2$rel.ab[sum.class.min.max.2$Taxa == levels(sum.class.min.max.2$Taxa)[[k]]])
}
print(d6[order(d6[,"mean"],decreasing = TRUE),])
}
```
##### order taxonomy
###### plot bar chart
```{r}
ab.Taxonomy <- ab.Taxonomy.order
tax.data2plot <- data.frame(Sample = ab.Taxonomy$Name, Freq = ab.Taxonomy$Freq, Taxa = ab.Taxonomy$Order, Class = ab.Taxonomy$Class, Type = ab.Taxonomy$Type, Ant = ab.Taxonomy$Ant, Age = ab.Taxonomy$Age, Tree = ab.Taxonomy$TreeR)
tax.data2plot <- ddply(tax.data2plot, c("Taxa", "Sample", "Type", "Ant", "Age", "Tree"), summarise, Freq = sum(Freq))
# rename "_unclassified"
tax.data2plot$Taxa <- gsub("_unclassified","_uncl.", tax.data2plot$Taxa)
tax.data2plot$Taxa <- gsub("_or","_uncl.", tax.data2plot$Taxa)
# for stacked bar charts
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Sample == tax.data2plot$Sample[i]])
tax.data2plot$Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
for (i in 1:nrow(tax.data2plot)) {
sample.sum <- sum(tax.data2plot$Freq[tax.data2plot$Ant == tax.data2plot$Ant[i]])
tax.data2plot$Ant.Rel.Ab[i] <- tax.data2plot$Freq[i] / sample.sum * 100}
## plot stacked bar chart per sample
AllSample.bar <- qplot(x=Sample,data=tax.data2plot,geom="bar",weight=Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllSample.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Epmean.order",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 10, height = 5, dpi = 300)
AllAnt.bar <- qplot(x=Ant,data=tax.data2plot,geom="bar",weight=Ant.Rel.Ab,fill=Taxa,main=plottitle) + scale_fill_manual(values=colPalette03) +
scale_y_continuous(expand = c(0,1)) + # reduced space between bars and x-axis
theme(axis.text.x = element_text(angle = 30, hjust = 1))
AllAnt.bar
ggsave(paste(PLOTS_DIR,"/bar.tax.Epmean.order.Ant",Sys.Date(),"pdf",sep = "."), useDingbats=FALSE,width = 6, height = 5, dpi = 300)
```
###### get statistics
```{r}
levels(factor(tax.data2plot$Taxa))
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="__")
sum.tax.taxSample <- aggregate(tax.data2plot$Rel.Ab, by=list(taxYZ=tax.data2plot$taxSample), FUN=sum)
head(sum.tax.taxSample)
# get statistics min mean max
sum.tax2check <- sum.tax.taxSample # sum.tax.taxType
split <- str_split_fixed(sum.tax2check$taxYZ, "__", 2)
sum.tax.min.max.2 <- cbind(split,sum.tax2check)
colnames(sum.tax.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.tax.min.max.2["Taxa"] <- lapply(sum.tax.min.max.2["Taxa"], as.factor)
d6 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d6) <- c("Taxa","min","mean", "max") # per site
for (k in 1:length(levels(sum.tax.min.max.2$Taxa))) {
d6[k,1] <- levels(sum.tax.min.max.2$Taxa)[[k]]
d6[k,2] <- min(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
d6[k,3] <- mean(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
d6[k,4] <- max(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
}
print(d6[order(d6[,"mean"],decreasing = TRUE),])
# Ant (Ep) variation
tax.data2plot$taxAnt <- paste(tax.data2plot$Taxa, tax.data2plot$Ant, sep="__")
sum.tax.taxAnt <- aggregate(tax.data2plot$Ant.Rel.Ab, by=list(taxYZ=tax.data2plot$taxAnt), FUN=sum)
sum(sum.tax.taxAnt$x)
# get statistics per ant species
sum.tax2check <- sum.tax.taxAnt # sum.tax.taxType
split <- str_split_fixed(sum.tax2check$taxYZ, "__", 2)
sum.tax.min.max.2 <- cbind(split,sum.tax2check)
colnames(sum.tax.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.tax.min.max.2["Taxa"] <- lapply(sum.tax.min.max.2["Taxa"], as.factor)
d7 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d7) <- c("Taxa","alf","con", "mean") # per site
for (k in 1:length(levels(sum.tax.min.max.2$Taxa))) {
d7[k,1] <- levels(sum.tax.min.max.2$Taxa)[[k]]
d7[k,2:3] <- sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]]
d7[k,4] <- mean(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
}
print(d7[order(d7[,"mean"],decreasing = TRUE),])
# sample variation
tax.data2plot$taxSample <- paste(tax.data2plot$Taxa, tax.data2plot$Sample, sep="_")
# get statistics min mean max per ant species
for (j in c("alf", "con")){
tax.data2plot.ant <- tax.data2plot[tax.data2plot$Ant == j,]
sum.tax.taxSample <- aggregate(tax.data2plot.ant$Rel.Ab, by=list(taxYZ=tax.data2plot.ant$taxSample), FUN=sum)
head(sum.tax.taxSample)
sum.tax2check <- sum.tax.taxSample # sum.tax.taxType
split <- str_split_fixed(sum.tax2check$taxYZ, "_", 2)
sum.tax.min.max.2 <- cbind(split,sum.tax2check)
colnames(sum.tax.min.max.2) <- c("Taxa", "YZ", "taxYZ", "rel.ab")
sum.tax.min.max.2["Taxa"] <- lapply(sum.tax.min.max.2["Taxa"], as.factor)
d8 <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(d8) <- c(paste0(j,".Taxa"),"min","mean", "max") # per site
for (k in 1:length(levels(sum.tax.min.max.2$Taxa))) {
d8[k,1] <- levels(sum.tax.min.max.2$Taxa)[[k]]
d8[k,2] <- min(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
d8[k,3] <- mean(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
d8[k,4] <- max(sum.tax.min.max.2$rel.ab[sum.tax.min.max.2$Taxa == levels(sum.tax.min.max.2$Taxa)[[k]]])
}
print(d8[order(d8[,"mean"],decreasing = TRUE),])
}
```
#### 3.2.4 Venn diagram - rel. ab.
##### OTU level - prevalent OTUs
```{r}
venn.var <- unique(as.character(meta(physeq4norm.ansys)$Ant))
print(venn.var)
list_core <- c() # an empty object to store information
prev <- 50 # min % of samples
det <- 0.4 # min % per sample
for (n in venn.var){ # for each variable n in Ants
#print(paste0("Identifying Core Taxa for ", n))
ps.sub <- subset_samples(physeq4norm.ansys, Ant == n) # Choose sample from Ants by n
core_m <- core_members(ps.sub, # ps.sub is phyloseq selected with only samples from g
detection = det,
prevalence = prev/100)
print(paste0("No. of core taxa in ", n, " : ", length(core_m))) # print core taxa identified in each Ants
list_core[[n]] <- core_m # add to a list core taxa for each group.
# print(list_core)
}
# subset the EPmean dataset extracting only widespread OTUs
(abundant.otus <- unique(c(list_core[[1]], list_core[[2]])))
length(abundant.otus) # No. of OTUs widespread in alf or con EPs
df.abundant <- as.data.frame(physeq4norm.ansys@tax_table@.Data)[rownames(physeq4norm.ansys@tax_table) %in% abundant.otus,]
head(df.abundant)
# average relative abundance of every OTU in the EPmean dataset
df.abundant$av.ab <- colSums(otu_table(physeq4norm.ansys)[,colnames(otu_table(physeq4norm.ansys)) %in% abundant.otus])/nrow(otu_table(physeq4norm.ansys))
head(df.abundant)
(abundant.percOfTotReads <- sum(df.abundant$av.ab)) # % of EPmean reads
plot(venn(list_core),
fills = c(alf="#d6e2e9", con="#cbf3f0"))
length(intersect(list_core$con, list_core$alf)) # no. of shared
length(unique(c(list_core$alf, list_core$con))) # no. of all valid taxa
length(intersect(list_core$con, list_core$alf))/length(unique(c(list_core$alf, list_core$con)))*100
(shared <- intersect(list_core$con, list_core$alf)) # shared
# shared OTUs account for X% of EPmean reads
shared.av.ab <- colSums(otu_table(physeq4norm.ansys)[,colnames(otu_table(physeq4norm.ansys)) %in% shared])/
nrow(otu_table(physeq4norm.ansys))
sum(shared.av.ab)
(alf <- setdiff(list_core$alf, list_core$con)) # only in alf
length(alf)
(con <- setdiff(list_core$con, list_core$alf)) # only in con
length(con)
(df.shared <- as.data.frame(physeq4norm.ansys@tax_table@.Data)[rownames(physeq4norm.ansys@tax_table) %in% shared,])
df.shared$av.ab <- shared.av.ab
df.shared.sort <- df.shared[order(df.shared$Phylum, df.shared$Class, df.shared$Order, df.shared$Family, df.shared$Genus),]
unique(df.shared.sort$Class)
(df.alf <- as.data.frame(physeq4norm.ansys@tax_table@.Data)[rownames(physeq4norm.ansys@tax_table) %in% alf,])
df.alf$av.ab <- colSums(otu_table(physeq4norm.ansys)[,colnames(otu_table(physeq4norm.ansys)) %in% alf])/
nrow(otu_table(physeq4norm.ansys))
df.alf.sort <- df.alf[order(df.alf$Phylum, df.alf$Class, df.alf$Order, df.alf$Family, df.alf$Genus),]
unique(df.alf.sort$Class)
(df.con <- as.data.frame(physeq4norm.ansys@tax_table@.Data)[rownames(physeq4norm.ansys@tax_table) %in% con,])
df.con$av.ab <- colSums(otu_table(physeq4norm.ansys)[,colnames(otu_table(physeq4norm.ansys)) %in% con])/
nrow(otu_table(physeq4norm.ansys))
df.con.sort <- df.con[order(df.con$Phylum, df.con$Class, df.con$Order, df.con$Family, df.con$Genus),]
unique(df.con.sort$Class)
df.widespread.sort <- cbind(
"OTU" = c(rownames(df.shared.sort), rownames(df.alf.sort), rownames(df.con.sort)),
"category" = c(rep("Shared",length(shared)), rep("A. alfari",length(alf)), rep("A. constructor",length(con))),
rbind(df.shared.sort, df.alf.sort, df.con.sort)
)