Chapter 3 MAG catalogue
3.1 Genome phylogeny
# Generate the phylum color heatmap
phylum_heatmap <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
arrange(match(genome, genome_tree$tip.label)) %>%
dplyr::select(genome,phylum) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome")
# Generate prevalence data
prevalence_data <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
group_by(genome,gut_location) %>%
summarise(presence=ifelse(sum(abundance)>0,1,0)) %>%
group_by(genome) %>%
summarise(prevalence=sum(presence))
# Generate F_colon heatmap
f_colon_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(gut_location =="F_colon") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate M_colon heatmap
m_colon_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(gut_location =="M_colon") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate D_ileum heatmap
d_ileum_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(gut_location =="D_ileum") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate K_ileum heatmap
k_ileum_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(gut_location =="K_ileum") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate Gut content heatmap
gut_content_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(sample_type =="Gut content") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate Gut tissue heatmap
gut_tissue_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(sample_type =="Gut tissue") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate tame heatmap
tame_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(fox_behaviour=="tame") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")
# Generate aggressive heatmap
aggressive_heatmap <- genome_counts_filt %>%
pivot_longer(!genome,names_to="sample",values_to="abundance") %>%
left_join(sample_metadata,by="sample") %>%
filter(fox_behaviour=="aggr") %>%
group_by(genome) %>%
summarise(presence=ifelse(sum(abundance)>0,"present","absent")) %>%
column_to_rownames(var="genome")# Generate basal tree
circular_tree <- force.ultrametric(genome_tree, method="extend") %>% # extend to ultrametric for the sake of visualisation
ggtree(., layout="fan", open.angle=10, size=0.2)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************# Add phylum ring
circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0, width=0.05, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add F_colon ring
circular_tree <- gheatmap(circular_tree, f_colon_heatmap, offset=0.2, width=0.05, colnames=FALSE) +
scale_fill_manual(values = c("absent" = "#ffffff", "present" = "#3D5C61")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add M_colon ring
circular_tree <- gheatmap(circular_tree, m_colon_heatmap, offset=0.3, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#ffffff","#41B6C0")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add D_ileum ring
circular_tree <- gheatmap(circular_tree, d_ileum_heatmap, offset=0.4, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#ffffff","#90C8C5")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add K_ileum ring
circular_tree <- gheatmap(circular_tree, k_ileum_heatmap, offset=0.5, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("absent" = "#ffffff", "present" = "#E5D388")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add Gut content ring
circular_tree <- gheatmap(circular_tree, gut_content_heatmap, offset=0.7, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("absent" ="#ffffff","present" = "#BFA366")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add Gut tissue ring
circular_tree <- gheatmap(circular_tree, gut_tissue_heatmap, offset=0.8, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#ffffff","#6E5244")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add prevalence ring
circular_tree <- circular_tree +
new_scale_fill() +
scale_fill_manual(values = "#cccccc") +
geom_fruit(
data=prevalence_data,
geom=geom_bar,
mapping = aes(x=prevalence, y=genome),
offset = 0.5,
orientation="y",
stat="identity")
# Add tame ring
circular_tree <- gheatmap(circular_tree, tame_heatmap, offset=1.4, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#ffffff","#bd70ae")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
# Add aggressive ring
circular_tree <- gheatmap(circular_tree, aggressive_heatmap, offset=1.5, width=0.05, colnames=FALSE) +
scale_fill_manual(values=c("#ffffff","#949293")) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0)) +
new_scale_fill()
#Plot circular tree
circular_tree %>% open_tree(30) %>% rotate_tree(90)
## Genome quality
tibble(Completeness=
paste0(round(genome_metadata$completeness %>% mean(),2),
"±",
round(genome_metadata$completeness %>% sd(),2)),
Contamination=
paste0(round(genome_metadata$contamination %>% mean(),2),
"±",
round(genome_metadata$contamination %>% sd(),2))) %>%
tt()| Completeness | Contamination |
|---|---|
| 92.6±6.75 | 1.92±2.11 |
#Generate quality biplot
genome_biplot <- genome_metadata %>%
dplyr::select(c(genome,phylum,completeness,contamination,length)) %>%
arrange(match(genome, rev(genome_tree$tip.label))) %>% #sort MAGs according to phylogenetic tree
ggplot(aes(x=completeness,y=contamination,size=length,color=phylum)) +
geom_point(alpha=0.7) +
xlim(c(70,100)) +
ylim(c(10,0)) +
scale_color_manual(values=phylum_colors) +
labs(y= "Contamination", x = "Completeness") +
theme_classic() +
theme(legend.position = "none")
#Generate contamination boxplot
genome_contamination <- genome_metadata %>%
ggplot(aes(y=contamination)) +
ylim(c(10,0)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_classic() +
theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
plot.margin = unit(c(0, 0, 0.40, 0),"inches")) #add bottom-margin (top, right, bottom, left)
#Generate completeness boxplot
genome_completeness <- genome_metadata %>%
ggplot(aes(x=completeness)) +
xlim(c(70,100)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_classic() +
theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
plot.margin = unit(c(0, 0, 0, 0.50),"inches")) #add left-margin (top, right, bottom, left)
#Render composite figure
#pdf("figures/completeness_contamination.pdf",width=10, height=5)
grid.arrange(grobs = list(genome_completeness,genome_biplot,genome_contamination),
layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1,1,1,4),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3)))
3.2 Functional overview
# Aggregate basal GIFT into elements
function_table <- genome_gifts %>%
to.elements(., GIFT_db)
# Generate basal tree
function_tree <- force.ultrametric(genome_tree, method="extend") %>%
ggtree(., size = 0.3) ***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************#Add phylum colors next to the tree tips
function_tree <- gheatmap(function_tree, phylum_heatmap, offset=0, width=0.1, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
#Add functions heatmap
function_tree <- gheatmap(function_tree, function_table, offset=0.5, width=3.5, colnames=FALSE) +
vexpand(.08) +
coord_cartesian(clip = "off") +
scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
labs(fill="GIFT")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
# Add completeness barplots
function_tree <- function_tree +
geom_fruit(data=genome_metadata,
geom=geom_bar,
grid.params=list(axis="x", text.size=2, nbreak = 1),
axis.params=list(vline=TRUE),
mapping = aes(x=length, y=genome, fill=completeness),
offset = 3.8,
orientation="y",
stat="identity") +
scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
labs(fill="Genome\ncompleteness")
function_tree
3.3 Functional ordination
# Generate the tSNE ordination
tSNE_function <- Rtsne(X=function_table, dims = 2, check_duplicates = FALSE)
# Plot the ordination
function_ordination <- tSNE_function$Y %>%
as.data.frame() %>%
mutate(genome=rownames(function_table)) %>%
inner_join(genome_metadata, by="genome") %>%
rename(tSNE1="V1", tSNE2="V2") %>%
dplyr::select(genome,phylum,tSNE1,tSNE2, length) %>%
ggplot(aes(x = tSNE1, y = tSNE2, color = phylum, size=length))+
geom_point(shape=16, alpha=0.7) +
scale_color_manual(values=phylum_colors) +
theme_minimal() +
labs(color="Phylum", size="Genome size") +
guides(color = guide_legend(override.aes = list(size = 5))) # enlarge Phylum dots in legend
function_ordination