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Pathogen-host-range.Rmd
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Pathogen-host-range.Rmd
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---
title: The phylogenetic range of bacterial and viral pathogens of vertebrates
output:
html_document:
css: custom.css
fig_height: 8
fig_width: 12
toc: true
fig_caption: true
code_folding: hide
pdf_document: default
editor_options:
chunk_output_type: console
---
**Paper**: The phylogenetic range of bacterial and viral pathogens of vertebrates
preprint: https://doi.org/10.1101/670315
**Authors**: Liam P. Shaw\*, Alethea Wang\*, David Dylus, Magda Meier, Grega Pogacnik, Christophe Dessimoz, Francois Balloux (\* co-first authors)
# Introduction
This is a supplementary R Markdown workbook (Supplementary Text 1) containing code to reproduce all main figures and analyses in the associated paper, as well as several supplementary files. Code can be shown or hidden overall (see top right) or for individual sections.
Code can be altered to change any of the figures. Some explanatory text is provided, but not much. Any questions can be addressed via email ([email protected]).
Unless otherwise stated, data related to viruses is plotted in <span style="color:red">red</span> and bacteria in black. The code should take <10 minutes to run on a standard laptop.
**Note:** At several points code is adapted/reused from the supplementary code repository made available by Olival et. al. (2017):
* Paper: *Host and Viral Traits Predict Zoonotic Spillover from Mammals* https://doi.org/10.1038/nature22975
* Code repository: https://github.com/ecohealthalliance/HP3
* MIT License: https://opensource.org/licenses/MIT
This mainly applies to several scripts in the `scripts' directory relating to the fitting and plotting of generalized additive models (GAMs) -- more information is included where appropriate. All code here is also made available under an MIT License.
# Preliminary steps
This section contains library requirements and defines two useful functions:
* PHB (Phylogenetic Host Breadth) - function of the cophenetic distance between the hosts of a pathogen species (default: mean).
* THR (Taxonomic Host Range) - function of the cophenetic distance between all hosts of a particular taxonomic group (default: mean).
```{r Setup, echo=FALSE, message=FALSE, cache=TRUE}
options(warn=-1) # Turn off warnings
def.chunk.hook <- knitr::knit_hooks$get("chunk")
knitr::knit_hooks$set(chunk = function(x, options) {
x <- def.chunk.hook(x, options)
ifelse(options$size != "normalsize", paste0("\n \\", options$size,"\n\n", x, "\n\n \\normalsize"), x)
})
# Libraries
library(broman)
library(ape)
library(ggplot2)
library(dplyr)
library(reshape2)
library(tidyr)
library(grid)
library(broman)
library(gridExtra)
library(cowplot)
cachedata = TRUE
cachetests = TRUE
echoing = TRUE
warnings = FALSE
# To (hopefully) avoid the accidental classic 'string/factor' bug
options(stringsAsFactors=F)
# Default plot theme
theme_basic <- function () {
theme_bw(base_size=14) %+replace%
theme(
axis.text=element_text(colour="black")
) %+replace%
theme(
panel.grid=element_blank()
)
}
# Function: Phylogenetic Host Breadth
PHB <- function(pathogen, cophenetic.matrix=cophenetic_matrix, m=pathogen_vs_host_db, FUN=mean){
species.list <- m[m$Species==pathogen,"HostSpeciesPHB"]
species.matrix <- cophenetic.matrix[species.list, species.list]
species.matrix <- species.matrix[upper.tri(species.matrix)]
if (length(species.matrix)>1){
phb.function <- FUN(species.matrix)
}
else{
phb.function <- 0
}
return(phb.function)
}
# Function: Taxonomic Host Range
THR <- function(taxgroup, cophenetic.matrix = cophenetic_matrix,
m = pathogen_vs_host_db, FUN = mean) {
species.list <- pathogen_vs_host_db[pathogen_vs_host_db$HostOrder==taxgroup,
"HostSpeciesPHB"]
thr.function <- FUN(cophenetic.matrix[species.list, species.list])
return(thr.function)
}
```
First, the data is read in: the database of host-pathogen associations (tidy format i.e. one association per row); and the host phylogenetic tree based on mitochondrial gene alignments.
```{r Read_in_data, cache=cachedata}
# Host-pathogen association database
pathogen_vs_host_db <- read.csv('data/PathogenVsHostDB-2019-05-30.csv', stringsAsFactors = F)
# Host phylogenetic tree
host_tree <- read.tree('data/HostPhylogeneticTree-2019-05-30.nwk')
# Uncomment to use only species which do not disrupt monophyly (n=40)
#host_tree <- read.tree('HostPhylogeneticTree-2019-05-30-pruned.nwk')
# Cophenetic matrix (from phylogenetic tree)
cophenetic_matrix <- cophenetic.phylo(host_tree)
# Keep only overlap
host_tree <- drop.tip(host_tree, host_tree$tip.label[which(!host_tree$tip.label %in% pathogen_vs_host_db$HostSpeciesPHB)])
pathogen_vs_host_db <- pathogen_vs_host_db[which(pathogen_vs_host_db$HostSpeciesPHB %in% host_tree$tip.label),]
# Subset database into virus and bacteria
virus <- pathogen_vs_host_db[which(pathogen_vs_host_db$Type=="Virus"),]
bacteria <- pathogen_vs_host_db[which(pathogen_vs_host_db$Type=="Bacteria"),]
```
### Initial statistics
To begin with, some overall statistics which are cited in the main text.
```{r Initial_statistics_host_orders, results=FALSE, cache=cachedata}
# List of host orders
host_orders <- unique(as.character(pathogen_vs_host_db$HostOrder))
n.host.orders <- length(host_orders)
```
```{r Initial_statistics_interhost, cache=cachedata}
# Calculate THR for each host order
order_thr <- sapply(host_orders, function(x) THR(x, FUN=max))
median.inter.host.distance.order <- median(order_thr)
```
The number of host orders is `r n.host.orders`.
The medium maximum inter-host distance within an order is `r require(broman); myround(median.inter.host.distance.order, 3)`.
#### Vector-borne
We find that viruses are significantly more likely to be vector-borne compared with bacteria.
```{r Initial_statistics_2, cache=cachedata}
vector.tab <- table(pathogen_vs_host_db[which(!duplicated(pathogen_vs_host_db$Species)),"VectorBorne"], pathogen_vs_host_db[which(!duplicated(pathogen_vs_host_db$Species)),"Type"])
vector.tab <- vector.tab[c("No", "Yes"),]
chisq.test(vector.tab)
```
#### Removing humans from the database
We want to think about pathogen range etc. after removing them from the database and the host tree. We recalculate PHB after doing this.
```{r Remove_humans, cache=cachedata}
# Removing humans from database
pathogen_vs_host_db_no_humans <- pathogen_vs_host_db[which(pathogen_vs_host_db$HostSpeciesPHB!="Homosapiens"),]
t.no.humans <- drop.tip(host_tree, tip = host_tree$tip.label[!host_tree$tip.label %in% pathogen_vs_host_db_no_humans$HostSpeciesPHB])
d.no.humans <- cophenetic.phylo(t.no.humans)
# Bacteria PHB
bacteria.names <- names(sort(table(bacteria$Species), decreasing=TRUE))
bacteria.no.humans.phbs <- as.numeric(sapply(bacteria.names, function(x) PHB(x,
m=pathogen_vs_host_db_no_humans,
cophenetic.matrix = d.no.humans)))
virus.names <- names(sort(table(virus$Species), decreasing=TRUE))
virus.no.humans.phbs <- as.numeric(sapply(virus.names, function(x) PHB(x, m=pathogen_vs_host_db_no_humans,
cophenetic.matrix = d.no.humans)))
virus.no.humans.phbs.range <- as.numeric(sapply(virus.names, function(x) PHB(x, m=pathogen_vs_host_db_no_humans,
cophenetic.matrix = d.no.humans, FUN = max)))
```
#### Summary statistics
It is useful to have data frames with the summary statistics for each pathogen species (i.e. one row per pathogen), calculated from the pathogen-host association database (one row per association). We separate this into viruses and bacteria, as we treat them separately.
### Viruses
```{r Virus_data_frame, results=FALSE, cache=cachedata}
options(warn=-1) # Turn off warnings
virus.names <- names(sort(table(virus$Species), decreasing=TRUE))
n.hosts <- as.numeric(sort(table(virus$Species), decreasing=TRUE))
virus.phbs <- sapply(virus.names, function(x) PHB(x))
virus.unique <- virus[which(!duplicated(virus$Species)),]
rownames(virus.unique) <- virus.unique$Species
proteins <- as.numeric(virus.unique[virus.names, "ProteinCount"])
virus.families <- virus.unique[virus.names, "Family"]
# Combine into a data frame of statistics for each viral pathogen
virus.df <- data.frame(cbind(virus.phbs, proteins))
genome.size <- as.numeric(virus.unique[virus.names, "Gsize"])
virus.df$genome.size <- genome.size
virus.df$family <- virus.families
#
n.hosts <- as.numeric(sort(table(virus$Species), decreasing=TRUE))
virus.df$n.hosts <- n.hosts
genome.type <- virus.unique[virus.names, "Genome"]
virus.df$genome.type <- genome.type # Genome type
# Genome type (just RNA/DNA)
genome.type.rna.dna <- ifelse(genome.type %in% c("(-) ssRNA", "(+) ssRNA",
"ssRNA-RT", "dsRNA"), "RNA",
ifelse(genome.type %in% c("dsDNA", "dsDNA-RT", "ssDNA"),
"DNA", ""))
virus.df$genome.type.rna.dna <- genome.type.rna.dna
zoonotic <- ifelse(virus.unique[virus.names, "Zoonotic"]=="Yes", "Zoonotic", "Not zoonotic")
virus.df$n.hosts <- n.hosts # Number of hosts
virus.df$zoonotic <- zoonotic # Zoonotic
virus.df$virus.phbs.no.human <- virus.no.humans.phbs # Add non-human mean PHB
virus.df$virus.phbs.max.no.human <- virus.no.humans.phbs.range # Add non-human maximum PHB
virus.df$vector.borne <- virus.unique[virus.names, "VectorBorne"]
```
### Bacteria
```{r Bacteria_data_frame, cache=cachedata}
# Get bacterial pathogen species names
bacteria.names <- names(sort(table(bacteria$Species), decreasing=TRUE))
# Get number of hosts for each pathogen
n.hosts <- as.numeric(sort(table(bacteria$Species), decreasing=TRUE))
# Get mean PHB for each pathogen
bacteria.phbs <- as.numeric(sapply(bacteria.names, function(x) PHB(x)))
# Dataframe of unique bacterial pathogen species
bacteria.unique <- bacteria[which(!duplicated(bacteria$Species)),]
rownames(bacteria.unique) <- bacteria.unique$Species
# Extract relevant lifestyle variables:
# motility, cell, Gram stain,
# spore formation, oxygen requirements, zoonotic
motility <- bacteria.unique[bacteria.names, "Motility"]
cell <- bacteria.unique[bacteria.names, "Cell"]
gram <- bacteria.unique[bacteria.names, "GramStain"]
spore <- bacteria.unique[bacteria.names, "Spore"]
oxygen <- bacteria.unique[bacteria.names, "Oxygen"]
zoonotic <- ifelse(bacteria.unique[bacteria.names, "Zoonotic"]=="Yes", "Zoonotic", "Not zoonotic")
# Combine this information into a single data frame of statistics for each bacterial pathogen
bacteria.df <- data.frame(cbind(bacteria.names, n.hosts, bacteria.phbs,
motility, gram, spore, oxygen, zoonotic, cell))
bacteria.df$bacteria.phbs <- as.numeric(as.character(bacteria.df$bacteria.phbs)) # Make sure numeric variable
bacteria.df$bacteria.phbs.no.human <- bacteria.no.humans.phbs # Add in PHB without humans
bacteria.df$family <- bacteria.unique[bacteria.names, "Family"] # Add taxonomic family
bacteria.df$genus <- bacteria.unique[bacteria.names, "Genus"] # Add taxonomic genus
bacteria.df$vector.borne <- bacteria.unique[bacteria.names, "VectorBorne"]
```
# Figures
## Figure 1
This is the schematic overview figure, and was made manually in Inkscape using images from FlatIcon (see references in main manuscript).
## Figure 2
We get a dataset of just the unique pathogens and then calculate the mean phylogenetic host breadth (PHB) of each pathogen. We then use this to plot a histogram.
```{r Figure_PHB_histogram, results='show', cache=cachedata}
options(warn=-1) # Turn off warnings
# Get unique pathogens
unique_pathogens <- data.frame(Species=pathogen_vs_host_db[!duplicated(pathogen_vs_host_db$Species),"Species"],
Type=pathogen_vs_host_db[!duplicated(pathogen_vs_host_db$Species),"Type"])
unique_pathogens$Species <- as.character(unique_pathogens$Species)
# Calculate PHB
unique_pathogens$PHB <- sapply(unique_pathogens$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_matrix, m = pathogen_vs_host_db))
unique_pathogens$PHB.median <- sapply(unique_pathogens$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_matrix, m = pathogen_vs_host_db, FUN=median))
unique_pathogens$PHB.max <- sapply(unique_pathogens$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_matrix, m = pathogen_vs_host_db, FUN = max))
# Number of total hosts
host_species_counts <- data.frame(pathogen_vs_host_db %>% group_by(Species) %>% summarise(count=length(HostSpeciesPHB)))
rownames(host_species_counts) <- host_species_counts$Species
unique_pathogens$N.hosts <- host_species_counts[unique_pathogens$Species,"count"]
# Gather data
pathogen_range_histo <- data.frame( phb = 1:50/30-0.03,
bacteria = hist(filter(unique_pathogens, Type == "Bacteria")$PHB, breaks = 0:50/30, freq = TRUE, plot = FALSE)$count,
virus = hist(filter(unique_pathogens, Type == "Virus")$PHB, breaks = 0:50/30, freq = TRUE, plot = FALSE)$count) %>% gather(type, count, -phb)
pathogen_range_histo$count.norm <- pathogen_range_histo$count/sum(pathogen_range_histo$count)
pathogen_range_histo$type <- ifelse(pathogen_range_histo$type=="bacteria", "Bacteria", "Virus")
pathogen_range_histo.total <- data.frame(pathogen_range_histo %>% group_by(phb) %>% summarise(count=sum(count)))
# Pathogen range histogram, excluding zeros
pathogen_range_histo$type <- ordered(pathogen_range_histo$type, levels=c("Virus", "Bacteria"))
levels(pathogen_range_histo$type) <- c("(a) Viruses", "(b) Bacteria")
p.histogram.figure.2 <- ggplot(pathogen_range_histo, aes(fill=type, x=phb, y=count))+
geom_histogram(colour="black", aes(fill="All"), data=pathogen_range_histo.total, stat="identity", alpha=0.4)+
ylim(c(0,75))+geom_bar(stat="identity")+
facet_wrap(~type, ncol=1)+
scale_fill_manual(values=c("#de2d26", "#252525", "#f7f7f7"))+
xlim(c(0,1))+theme_basic()+
xlab("Mean PHB")+
ylab("Frequency")+
labs(fill="Pathogen type")+
geom_vline(xintercept = median(filter(unique_pathogens, Type == "Bacteria")$PHB[which(filter(unique_pathogens, Type == "Bacteria")$PHB!=0)]),
colour="white", size=2, alpha=0.8)+
geom_vline(xintercept = median(filter(unique_pathogens, Type == "Bacteria")$PHB[which(filter(unique_pathogens, Type == "Bacteria")$PHB!=0)]),
colour="black", size=1, linetype="dashed")+ # Second to make colour clear
geom_vline(xintercept = median(filter(unique_pathogens, Type == "Virus")$PHB[which(filter(unique_pathogens, Type == "Virus")$PHB!=0)]),
colour="white", size=2, alpha=0.8)+
geom_vline(xintercept = median(filter(unique_pathogens, Type == "Virus")$PHB[which(filter(unique_pathogens, Type == "Virus")$PHB!=0)]),
colour="red", size=1, linetype="dashed")+ # Second to make colour clear
theme(axis.text=element_text(size=16),
legend.text =element_text(colour="black", size=16),
legend.title=element_text(colour="black", size=18),
strip.text.x=element_text(colour="black", size=22),
axis.title=element_text(colour="black", size=22))+
theme(legend.position="none")
# Perform Wilcox test (for the caption of the figure)
wilcox.test(unique_pathogens[which(unique_pathogens$PHB>0 & unique_pathogens$Type=="Bacteria"),"PHB"], unique_pathogens[which(unique_pathogens$PHB>0 & unique_pathogens$Type=="Virus"),"PHB"])
median(unique_pathogens[which(unique_pathogens$PHB>0 & unique_pathogens$Type=="Bacteria"),"PHB"])
median(unique_pathogens[which(unique_pathogens$PHB>0 & unique_pathogens$Type=="Virus"),"PHB"])
# Show plot
p.histogram.figure.2
# Save figure
pdf("figures/Figure-2-histogram-PHB.pdf", width=12, height=8)
p.histogram.figure.2
dev.off()
png("figures/Figure-2-histogram-PHB.png", width=1200, height=800)
p.histogram.figure.2
dev.off()
```
### Subsampling to non-human hosts
To examine whether sampling bias could be influencing this result, we look at the effect of subsampling to only non-human hosts (both domestic and non-domestic).
```{r Figure_PHB_histogram_subsample_function, cache=cachedata}
options(warn=-1) # Turn off warnings
# Function to plot PHB histogram for a given dataset
plotHistogram <- function(association_dataset, cophenetic_distances){
# Get unique pathogens
unique_pathogens.subsampled <- data.frame(Species=association_dataset[!duplicated(association_dataset$Species),"Species"],
Type=association_dataset[!duplicated(association_dataset$Species),"Type"])
unique_pathogens.subsampled$Species <- as.character(unique_pathogens.subsampled$Species)
# Calculate PHB
unique_pathogens.subsampled$PHB <- sapply(unique_pathogens.subsampled$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_distances, m = association_dataset))
unique_pathogens.subsampled$PHB.median <- sapply(unique_pathogens.subsampled$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_distances, m = association_dataset, FUN=median))
unique_pathogens.subsampled$PHB.max <- sapply(unique_pathogens.subsampled$Species, function(x) PHB(x, cophenetic.matrix = cophenetic_distances, m = association_dataset, FUN = max))
# Number of total hosts
host_species_counts <- data.frame(association_dataset %>% group_by(Species) %>% summarise(count=length(HostSpeciesPHB)))
rownames(host_species_counts) <- host_species_counts$Species
unique_pathogens.subsampled$N.hosts <- host_species_counts[unique_pathogens.subsampled$Species,"count"]
# Gather data
pathogen_range_histo <- data.frame(phb = 1:50/30-0.03,
bacteria = hist(filter(unique_pathogens.subsampled, Type == "Bacteria")$PHB, breaks = 0:50/30, freq = TRUE, plot = FALSE)$count,
virus = hist(filter(unique_pathogens.subsampled, Type == "Virus")$PHB, breaks = 0:50/30, freq = TRUE, plot = FALSE)$count) %>% pivot_longer(cols=c("bacteria", "virus"), names_to = "type", values_to = "count")
pathogen_range_histo$count.norm <- pathogen_range_histo$count/sum(pathogen_range_histo$count)
pathogen_range_histo$type <- ifelse(pathogen_range_histo$type=="bacteria", "Bacteria", "Virus")
pathogen_range_histo.total <- data.frame(pathogen_range_histo %>% group_by(phb) %>% summarise(count=sum(count)))
# Pathogen range histogram, excluding zeros
pathogen_range_histo$type <- ordered(pathogen_range_histo$type, levels=c("Virus", "Bacteria"))
levels(pathogen_range_histo$type) <- c("(a) Viruses", "(b) Bacteria")
p.histogram <- ggplot(pathogen_range_histo, aes(fill=type, x=phb, y=count))+
geom_histogram(colour="black", aes(fill="All"), data=pathogen_range_histo.total, stat="identity", alpha=0.4)+
ylim(c(0,75))+geom_bar(stat="identity")+
facet_wrap(~type, ncol=1)+
scale_fill_manual(values=c("#de2d26", "#252525", "#f7f7f7"))+
xlim(c(0,1))+theme_basic()+
xlab("Mean PHB")+
ylab("Frequency")+
labs(fill="Pathogen type")+
geom_vline(xintercept = median(filter(unique_pathogens.subsampled, Type == "Bacteria")$PHB[which(filter(unique_pathogens.subsampled, Type == "Bacteria")$PHB!=0)]),
colour="white", size=2, alpha=0.8)+
geom_vline(xintercept = median(filter(unique_pathogens.subsampled, Type == "Bacteria")$PHB[which(filter(unique_pathogens.subsampled, Type == "Bacteria")$PHB!=0)]),
colour="black", size=1, linetype="dashed")+ # Second to make colour clear
geom_vline(xintercept = median(filter(unique_pathogens.subsampled, Type == "Virus")$PHB[which(filter(unique_pathogens.subsampled, Type == "Virus")$PHB!=0)]),
colour="white", size=2, alpha=0.8)+
geom_vline(xintercept = median(filter(unique_pathogens.subsampled, Type == "Virus")$PHB[which(filter(unique_pathogens.subsampled, Type == "Virus")$PHB!=0)]),
colour="red", size=1, linetype="dashed")+ # Second to make colour clear
theme(axis.text=element_text(size=16),
legend.text =element_text(colour="black", size=16),
legend.title=element_text(colour="black", size=18),
strip.text.x=element_text(colour="black", size=22),
axis.title=element_text(colour="black", size=22))+
theme(legend.position="none")
return(p.histogram)
}
```
```{r Figure_PHB_domestic, cache=cachedata}
options(warn=-1) # Turn off warnings
pathogen_vs_host_db_domestic <- pathogen_vs_host_db[which(pathogen_vs_host_db$Domestic=="Yes" & pathogen_vs_host_db$HostSpeciesPHB!="Homosapiens"),]
cophenetic_domestic <- cophenetic_matrix[which(rownames(cophenetic_matrix) %in% pathogen_vs_host_db_domestic$HostSpeciesPHB), which(colnames(cophenetic_matrix) %in% pathogen_vs_host_db_domestic$HostSpeciesPHB)]
p.PHB.domestic <- plotHistogram(pathogen_vs_host_db_domestic, cophenetic_domestic)
```
```{r Figure_PHB_non_domestic, cache=cachedata}
options(warn=-1) # Turn off warnings
pathogen_vs_host_db_non_domestic <- pathogen_vs_host_db[which(pathogen_vs_host_db$Domestic=="No" & pathogen_vs_host_db$HostSpeciesPHB!="Homosapiens"),]
cophenetic_non_domestic <- cophenetic_matrix[which(rownames(cophenetic_matrix) %in% pathogen_vs_host_db_non_domestic$HostSpeciesPHB), which(colnames(cophenetic_matrix) %in% pathogen_vs_host_db_non_domestic$HostSpeciesPHB)]
p.PHB.nondomestic <- plotHistogram(pathogen_vs_host_db_non_domestic, cophenetic_non_domestic)
```
```{r Figure_PHB_subsampling_combine, cache=cachedata}
cowplot::plot_grid(p.PHB.domestic+ggtitle("Domestic (n=2,270)"), p.PHB.nondomestic + ggtitle("Non-domestic (n=5,898)"))
```
## Figure 3
We produce an analagous version of Figure 1 from Olival et al. (2017) based on our dataset. Ordering of mammal orders is the same to allow easy comparison with their viral figure.
```{r viral_richness_per_host_order, cache=cachedata}
options(warn=-1) # Turn off warnings
# First make a dataset per order of mammals
# Note that we have Artiodactyla where Olival et al. have "Cetartiodactyla" - have changed this
mammal.orders <- stringi::stri_trans_totitle(c( "CINGULATA", "PILOSA","DIDELPHIMORPHIA", "EULIPOTYPHLA",
"CHIROPTERA", "PRIMATES", "RODENTIA", "CARNIVORA", "LAGOMORPHA", "PROBOSCIDEA", "DIPROTODONTIA",
"ARTIODACTYLA", "PERISSODACTYLA", "PERAMELEMORPHIA", "SCANDENTIA"))
pathogen_vs_mammal_db <- pathogen_vs_host_db_no_humans[which(pathogen_vs_host_db_no_humans$HostOrder %in% mammal.orders),]
human_pathogens <- pathogen_vs_host_db$Species[which(pathogen_vs_host_db$HostSpeciesPHB=="Homosapiens")]
pathogen_vs_mammal_db$pathogenSharedWithHumans <- ifelse(pathogen_vs_mammal_db$Species %in% human_pathogens,
"yes", "no")
boxplot.df <- data.frame(pathogen_vs_mammal_db %>%
group_by(HostSpeciesPHB, HostOrder, Type) %>%
summarise(nPathogens=length(Species),
nHumanPathogens=sum(pathogenSharedWithHumans == "yes"),
nWildMammalHosts=sum(Domestic == "No")))
boxplot.df$propHumanPathogens <- boxplot.df$nHumanPathogens/boxplot.df$nPathogens
boxplot.df$propWildMammalHosts <- boxplot.df$nWildMammalHosts/boxplot.df$nPathogens
# Add information on domesticity: is host species domestic or not?
# Need to check this as they can be different for each association
# i.e. is a species completely wild, completely domestic, or a mixture?
host.df <- pathogen_vs_host_db[which(!duplicated(pathogen_vs_host_db$HostSpeciesPHB)),]
host.df.domestic <- host.df$Domestic
names(host.df.domestic) <- host.df$HostSpeciesPHB
domestic.mammals <- names(host.df.domestic)[which(host.df.domestic=="Yes")]
wild.mammals <- names(host.df.domestic)[which(host.df.domestic=="No")]
captive.mammals <- names(host.df.domestic)[which(host.df.domestic=="Captive")]
# Order hosts using Olival et al. ordering
boxplot.df$HostOrder <- ordered(boxplot.df$HostOrder, levels=mammal.orders)
# Proportion of zoonotic pathogens for viruses
p.virus.zoonotic <- ggplot(data=boxplot.df[which(boxplot.df$Type=="Virus"),], aes( HostOrder, propHumanPathogens))+
geom_boxplot(outlier.shape = NA, fill="#fee5d9")+
theme_basic()+coord_flip()+
geom_jitter(aes(fill=propWildMammalHosts), colour="black", shape=21, width=0.15, height = 0, size=2)+
scale_fill_continuous(low="white", high="red")+
scale_x_discrete(drop=FALSE)+
theme(legend.position = "none")+
ylab("")+xlab("")+
ylab("Proportion of zoonotic viruses")+
ggtitle("(a)")+
theme(plot.title=element_text(hjust=0))
p.virus.richness <- ggplot(data=boxplot.df[which(boxplot.df$Type=="Virus"),], aes( HostOrder, nPathogens))+
geom_boxplot(outlier.shape = NA, fill="#fee5d9")+
theme_basic()+coord_flip()+
geom_jitter(aes(fill=propWildMammalHosts), colour="black", shape=21, width=0.15, height = 0, size=2)+
scale_fill_continuous(low="white", high="red", limits=c(0,1))+
ylab("")+xlab("")+
theme(legend.position = "none")+
theme(axis.text.y=element_blank())+
ylab("Total viral richness")+
ggtitle("(b)")+
theme(plot.title=element_text(hjust=0))+
ylim(c(0,60))+
labs(fill="Proportion of wild hosts")+
theme(legend.position = c(0.7,0.15),
legend.title = element_text(size=14),
legend.background = element_rect(colour="black"))
p.bacteria.zoonotic <- ggplot(data=boxplot.df[which(boxplot.df$Type=="Bacteria"),], aes( HostOrder, propHumanPathogens))+
geom_boxplot(outlier.shape = NA, fill="#f7f7f7")+
theme_basic()+coord_flip()+
geom_jitter(aes(fill=propWildMammalHosts), colour="black", shape=21, width=0.15, height = 0, size=2)+
scale_x_discrete(drop=FALSE)+
scale_fill_continuous(low="white", high="black")+
ylab("")+xlab("")+
theme(legend.position = "none")+
ylab("Proportion of zoonotic bacteria")+
ggtitle("(c)")+
theme(plot.title=element_text(hjust=0))
p.bacteria.richness <- ggplot(data=boxplot.df[which(boxplot.df$Type=="Bacteria"),], aes( HostOrder, nPathogens))+
geom_boxplot(outlier.shape = NA, fill="#f7f7f7")+
theme_basic()+coord_flip()+
geom_jitter(aes(fill=propWildMammalHosts), colour="black", shape=21, width=0.15, height = 0, size=2)+
scale_fill_continuous(low="white", high="black", limits=c(0,1))+
ylab("")+xlab("")+
scale_x_discrete(drop=FALSE)+
theme(legend.position = "none")+
theme(axis.text.y=element_blank())+
ylab("Total bacterial richness")+
ggtitle("(d)")+
theme(plot.title=element_text(hjust=0))+
ylim(c(0,60))+
labs(fill="Proportion of wild hosts")+
theme(legend.position = c(0.7,0.15),
legend.title = element_text(size=14),
legend.background = element_rect(colour="black"))
pdf(file="figures/Figure-3-richness-per-species.pdf", width=20, height=9)
cowplot::plot_grid(p.virus.zoonotic, p.virus.richness,
p.bacteria.zoonotic, p.bacteria.richness, nrow=1)
dev.off()
png(file="figures/Figure-3-richness-per-species.png", width=2000, height=900)
cowplot::plot_grid(p.virus.zoonotic, p.virus.richness,
p.bacteria.zoonotic, p.bacteria.richness, nrow=1)
dev.off()
# Show plot
cowplot::plot_grid(p.virus.zoonotic, p.virus.richness,
p.bacteria.zoonotic, p.bacteria.richness, nrow=1)
# Correlation of bacterial and viral richness per host order
boxplot.df.bacteria <- boxplot.df[which(boxplot.df$Type=="Bacteria"),]
boxplot.df.virus <- boxplot.df[which(boxplot.df$Type=="Virus"),]
# Shared hosts i.e. have both bacteria and virus associations
shared.hosts <- boxplot.df.virus$HostSpeciesPHB[which(boxplot.df.virus$HostSpeciesPHB %in% boxplot.df.bacteria$HostSpeciesPHB)]
boxplot.df.shared <- boxplot.df[which(boxplot.df$HostSpeciesPHB %in% shared.hosts),]
# Now they are sorted, so can easily separate
mammal.bacterial.viral.richness <- data.frame(species=shared.hosts,
nBacteria=boxplot.df.shared$nPathogens[which(boxplot.df.shared$Type=="Bacteria")],
propBacteriaSharedHumans=boxplot.df.shared$propHumanPathogens[which(boxplot.df.shared$Type=="Bacteria")],
nVirus=boxplot.df.shared$nPathogens[which(boxplot.df.shared$Type=="Virus")],
propVirusSharedHumans=boxplot.df.shared$propHumanPathogens[which(boxplot.df.shared$Type=="Virus")])
cor.test(mammal.bacterial.viral.richness$nBacteria, mammal.bacterial.viral.richness$nVirus)
```
## Figure 4
We fit generalized additive models (GAMs) for host traits which predict viral and bacterial richness. To do this we use a dataset of host traits collected by Olival et al. to predict pathogen richness (bacterial and viral) per species. These host traits included a phylogenetic eigenvector regression (PVR) of body mass. As Olival et al. collected information for an analysis of only viral pathogens, we found there was better overlap for viruses in our dataset (n=613 host wild mammals) than for bacteria (n=274).
```{r GAMs, cache=cachedata, message=FALSE}
options(warn=-1) # Turn off warnings
source('scripts/04-fit-GAMs-host-traits.R')
source('scripts/05-plot-GAMs-host-traits.R')
allplots
```
## Figure 5
We also fit GAMs to predict zoonotic potential. For bacteria, GAMs could include terms for host range (PHB_mean, PHB_median, or PHB_max), research effort (NCBI PubMed, Nucleotide, or SRA results), motility, sporulation, being vector-borne, oxygen requirements, and Gram stain. We excluded cellular lifestyle (intra/extracellular) as a predictor due to low numbers, and excluded pathogens of unknown motility (n=50) or sporulation (n=17). For viruses, GAMs could include terms for host range, research effort, genome size (number of proteins and length), being vector-borne, and genome type (Baltimore classification). We excluded pathogens with unknown genome size (n=253). We observed structure in some partial effect residuals in the best-fit GAMs: research effort for bacteria (Figure 6b) and host range for viruses (Figure 6d). This structure was driven by pathogen taxonomy, with families (orders) for bacteria (viruses) having different zoonotic potential; e.g. the Staphylococcaceae contain a high proportion of generalists. Attempts to include taxonomy as a categorical predictor produced best fit models which excluded all lifestyle factors (not shown), although host range and research effort were still the strongest predictors.
```{r GAMs-zoonotic-potential, cache=cachedata, message=FALSE}
detachAllPackages <- function() {
basic.packages <- c("package:stats","package:graphics","package:grDevices","package:utils","package:datasets","package:methods","package:base")
package.list <- search()[ifelse(unlist(gregexpr("package:",search()))==1,TRUE,FALSE)]
package.list <- setdiff(package.list,basic.packages)
if (length(package.list)>0) for (package in package.list) detach(package, character.only=TRUE)
}
```
```{r GAMs-2, message=FALSE, cache=cachedata, message=FALSE}
detachAllPackages()
options(warn=-1)
library(stringi)
library(parallel)
library(magrittr)
library(purrr)
library(mgcv)
library(ggplot2)
library(cowplot)
library(viridis)
library(svglite)
library(phangorn)
library(MuMIn)
library(dplyr)
source('scripts/06-fit-GAM-viral-zoonotic-potential.R')
source('scripts/07-fit-GAM-bacterial-zoonotic-potential.R')
```
```{r GAMs-2-plot, cache=cachedata}
allplots = cowplot::plot_grid(allplots_viral, allplots_bacterial, nrow=2, rel_widths = c(5.3, 5.3, 5.3))
# Save plots
png(file="figures/Figure-5-gams-zoonotic-potential.png", width = convertr::convert(183, "mm", "in")*500, convertr::convert(100, "mm", "in")*600, pointsize=7, res=600)
allplots
dev.off()
pdf(file="figures/Figure-5-gams-zoonotic-potential.pdf", width =7, height=5, pointsize=7)
allplots
dev.off()
# Show plot
allplots
```
## Figure 6
We make a plot of the host switching for bacteria and viruses together (left panel of figure).
If we denote the set of pathogens seen at least once in a host taxon a as p_a (where the taxon could be a species, genus, family etc.), we define the fraction of shared pathogens between two taxa a and b as
```{latex}
s_(a,b) = \frac{| p_{a} \union p_b |}{p_{a} \intersection p_{b}}
```
Note that this definition is symmetric in a, b. It can therefore be compared with the (mean) phylogenetic distance between taxa using a Mantel test to determine the correlation.
```{r Figure_distance_shared_pathogens_scatter, cache=cachedata}
hostShiftingResultsOrder <- function(type="Bacteria", min.rep=0){
metadata.type <- metadata[which(metadata$Type %in% type),]
# Get host orders, and keep only those with more than min.rep representatives
type.host.orders.t <- table(metadata.type$HostOrder)
type.host.orders <- names(type.host.orders.t[type.host.orders.t>min.rep])
# Prepare results to fill
results <- matrix(nrow=0, ncol=8)
# For each host order
for (reference.order in type.host.orders){
reference.order.pathogens <- unique(metadata.type[which(metadata.type$HostOrder==reference.order), "Species"])
reference.order.species <- unique(as.character(metadata.type[which(metadata.type$HostOrder==reference.order), "HostSpeciesPHB"]))
# For each host order, return percentage of pathogens shared with host order vs. mean distance
for (comparison.order in type.host.orders){
# All pathogens in that host order
pathogens <- metadata.type[which(metadata.type$HostOrder==comparison.order),"Species"]
# Percentage of those pathogens that are shared with reference order
perc.shared <- length(pathogens[pathogens %in% reference.order.pathogens])/length(pathogens)
perc.shared.unique <- length(unique(pathogens)[unique(pathogens) %in% reference.order.pathogens])/length(unique(c(pathogens, reference.order.pathogens)))
# Mean of all host order in order from reference order
comparison.order.species <- unique(as.character(metadata.type[which(metadata.type$HostOrder==comparison.order),"HostSpeciesPHB"]))
mean.distance <- mean(d[comparison.order.species, reference.order.species])
sd.distance <- sd(d[comparison.order.species, reference.order.species])
# Add to results
if (reference.order==comparison.order){
mean.distance <- NA
sd.distance <- NA
perc.shared <- NA
perc.shared.unique <- NA
}
results <- rbind(results, c(reference.order,
comparison.order,
mean.distance,
sd.distance,
perc.shared,
perc.shared.unique,
length(reference.order.species),
length(comparison.order.species) ))
}
}
# Naming of data frame, add names of host orders
results <- as.data.frame(results, stringsAsFactors=FALSE)
colnames(results) <- c("order.1",
"order.2",
"mean.distance",
"sd.distance",
"perc.shared",
"perc.shared.unique",
"n.associations.order.1",
"n.associations.order.2")
results$perc.shared <- as.numeric(results$perc.shared)
results$perc.shared.unique <- as.numeric(results$perc.shared.unique)
results$mean.distance <- as.numeric(results$mean.distance)
results$sd.distance <- as.numeric(results$sd.distance)
return(results)
}
hostShiftingResultsFamily <- function(type="Bacteria", min.rep=0){
metadata.type <- metadata[which(metadata$Type %in% type),]
# Get host families, and keep only those with more than min.rep representatives
type.host.families.t <- table(metadata.type$HostFamily)
type.host.families <- names(type.host.families.t[type.host.families.t>min.rep])
# Get list of host.species
host.table <- table(metadata.type$HostSpeciesPHB)
host.species <- names(host.table)[which(host.table>min.rep)]
results <- matrix(nrow=0, ncol=8)
# For each host family
for (reference.family in type.host.families){
reference.family.pathogens <- unique(metadata.type[which(metadata.type$HostFamily==reference.family), "Species"])
reference.family.species <- unique(as.character(metadata.type[which(metadata.type$HostFamily==reference.family), "HostSpeciesPHB"]))
# For each host order, return percentage of pathogens shared with host family vs. mean distance
for (comparison.family in type.host.families){
# All pathogens in that host family
pathogens <- metadata.type[which(metadata.type$HostFamily==comparison.family),"Species"]
# Percentage of those pathogens that are shared with reference order
perc.shared <- length(pathogens[pathogens %in% reference.family.pathogens])/length(pathogens)
perc.shared.unique <- length(unique(pathogens)[unique(pathogens) %in% reference.order.pathogens])/length(unique(c(pathogens, reference.order.pathogens)))
# Mean of all host order in order from reference order
comparison.family.species <- unique(as.character(metadata.type[which(metadata.type$HostFamily==comparison.family),"HostSpeciesPHB"]))
mean.distance <- mean(d[comparison.family.species, reference.family.species])
sd.distance <- sd(d[comparison.family.species, reference.family.species])
# Add to results
results <- rbind(results, c(reference.family,
comparison.family,
mean.distance,
sd.distance,
perc.shared,
perc.shared.unique,
length(metadata.type[which(metadata.type$HostFamily==reference.family), "HostSpeciesPHB"]),
length(metadata.type[which(metadata.type$HostFamily==comparison.family), "HostSpeciesPHB"]) ))
}
}
# Naming of data frame, add names of host orders
results <- as.data.frame(results, stringsAsFactors=FALSE)
colnames(results) <- c("family.1",
"family.2",
"mean.distance",
"sd.distance",
"perc.shared",
"perc.shared.unique",
"n.associations.family.1",
"n.associations.order.2")
results$perc.shared <- as.numeric(results$perc.shared)
results$perc.shared.unique <- as.numeric(results$perc.shared.unique)
results$mean.distance <- as.numeric(results$mean.distance)
results$sd.distance <- as.numeric(results$sd.distance)
return(results)
}
hostShiftingResultsSpecies <- function(type="Bacteria", min.rep=0){
metadata.type <- metadata[which(metadata$Type %in% type),]
# Get host orders, and keep only those with more than min.rep representatives
type.host.species.t <- table(metadata.type$HostSpeciesPHB)
type.host.species <- names(type.host.species.t[type.host.species.t>min.rep])
# Get list of host.species
host.species <- type.host.species
results <- matrix(nrow=0, ncol=8)
# For each host species
for (reference.species in host.species){
reference.species.pathogens <- unique(metadata.type[which(metadata.type$HostSpeciesPHB==reference.species), "Species"])
# For each host species, return percentage of pathogens shared with host species vs. mean distance
for (comparison.species in host.species){
# All pathogens in that host species
pathogens <- metadata.type[which(metadata.type$HostSpeciesPHB==comparison.species),"Species"]
# Percentage of those pathogens that are shared with reference species
perc.shared <- length(pathogens[pathogens %in% reference.species.pathogens])/length(pathogens)
perc.shared.unique <- length(unique(pathogens)[unique(pathogens) %in% reference.species.pathogens])/length(unique(c(pathogens, reference.species.pathogens)))
# Mean of all host species in order from reference order
mean.distance <- mean(d[comparison.species, reference.species])
sd.distance <- sd(d[comparison.species, reference.species])
# Add to results
results <- rbind(results, c(reference.species,
comparison.species,
mean.distance,
sd.distance,
perc.shared,
perc.shared.unique,
length(reference.species),
length(comparison.species) ))
}
}
# Naming of data frame, add names of host orders
results <- as.data.frame(results, stringsAsFactors=FALSE)
colnames(results) <- c("species.1",
"species.2",
"mean.distance",
"sd.distance",
"perc.shared",
"perc.shared.unique",
"n.hosts.species.1",
"n.hosts.species.2")
results$perc.shared <- as.numeric(results$perc.shared)
results$perc.shared.unique <- as.numeric(results$perc.shared.unique)
results$mean.distance <- as.numeric(results$mean.distance)
results$sd.distance <- as.numeric(results$sd.distance)
return(results)
}
hostShiftingPlot <- function(results, title.string=""){
# Correlation test
correlation <- cor.test(results$mean.distance, results$perc.shared, method = "spearman")
# Label for correlation statistics
corr.label <- data.frame(
mean.distance = 0,
perc.shared = 0.1,
label = paste("Spearman's rho = ", myround(correlation$estimate, 3),
"\n p = ", myround(correlation$p.value, 3)),
host.order="",
n.hosts="NA*")
# Plot results
p <- ggplot(results, aes(x=mean.distance, y=perc.shared.unique))+
geom_point( fill="black", alpha=0.2)+
stat_smooth(method="loess", aes(group=1), se = FALSE, size=2, alpha=0.5)+
xlim(c(0,max(results$mean.distance)))+
ylim(c(0,1))+
xlab(paste("Mean phylogenetic distance"))+
ylab(paste("Fraction of shared pathogens"))+
theme_basic()+
labs(size="Number of host-pathogen associations")+
theme(axis.text=element_text(size=16),
legend.text =element_text(colour="black", size=16),
legend.title=element_text(colour="black", size=18),
strip.text.x=element_text(colour="black", size=22),
axis.title=element_text(colour="black", size=22))
return(p)
}
metadata <- pathogen_vs_host_db
d <- cophenetic_matrix
results.orders.10 <- hostShiftingResultsOrder(type=c("Bacteria", "Virus"), min.rep=10)
p.pathogen.sharing.orders.10 <- hostShiftingPlot(results.orders.10)+xlab("Mean phylogenetic distance between orders")
results.orders.5 <- hostShiftingResultsOrder(type=c("Bacteria", "Virus"), min.rep=5)
p.pathogen.sharing.orders.5 <- hostShiftingPlot(results.orders.5)
results.species.10 <- hostShiftingResultsSpecies(type=c("Bacteria", "Virus"), min.rep=10)
# plot, but exclude comparisons of species with themselves, and the outliers
p.pathogen.sharing.species.10 <- hostShiftingPlot(results.species.10[which(results.species.10$mean.distance!=0 &
results.species.10$mean.distance<1.8),])
```
We also carry out a Mantel test for correlation of the two distance matrices.
```{r mantel_test_correlation_distace, cache=cachedata}
mean.dist <- acast(species.1 ~ species.2, value.var = "mean.distance", data=results.species.10)
perc.shared <- acast(species.1 ~ species.2, value.var = "perc.shared.unique", data=results.species.10)
mantel.test(mean.dist, perc.shared)
```
We construct the right panel of the figure. We make a separate data frame for bacteria and for viruses, then combine these together and plot the results.
```{r Figure_distance_shared_pathogens_decay, cache=cachedata}
host.orders <- table(metadata$HostOrder)
min.rep.10 <- names(host.orders)[host.orders>10]
hostShiftingPlotDataHuman <- function(type=c("Bacteria", "Virus"), reference.species="Homosapiens", host.orders = min.rep.10, title.string=""){
# Limit to only pathogens of interest (bacteria, viruses, or both)
metadata.type <- metadata[which(metadata$Type %in% type),]
# Get host orders, and keep only those with more than min.rep representatives
type.host.orders <- min.rep.10
# Get list of reference.species pathogens
reference.species.pathogens <- unique(metadata.type[which(metadata.type$HostSpeciesPHB==reference.species), "Species"])
# For each host order, return percentage of pathogens shared with humans vs. mean distance of host species from reference species
results <- matrix(nrow=0, ncol=3)
for (b in type.host.orders){
# All pathogens in that host order, excluding the reference species
pathogens <- unique(metadata.type[which(metadata.type$HostOrder==b & metadata.type$HostSpeciesPHB!=reference.species),"Species"])
# Percentage of those pathogens that are shared with humans
perc.shared <- length(pathogens[pathogens %in% reference.species.pathogens])/length(pathogens)
# Mean of all host species in order from reference species. Note that we consider all species,
# not just those that have a pathogen association of the right type
host.species <- as.character(metadata[which(metadata$HostOrder==b),"HostSpeciesPHB"])
mean.distance <- mean(d[host.species, reference.species])
# Add to results
results <- rbind(results, c(perc.shared, mean.distance, length(unique(host.species))))
}
# Naming of data frame, add names of host orders
results <- as.data.frame(results)
colnames(results) <- c("perc.shared", "mean.distance", "n.hosts")
results$host.order <- type.host.orders
# Correlation test
correlation <- cor.test(results$mean.distance, results$perc.shared, method = "spearman")
# Label for correlation statistics
corr.label <- data.frame(
mean.distance = 0,
perc.shared = 0.1,
label = paste("Spearman's rho = ", myround(correlation$estimate, 3),
"\n p = ", myround(correlation$p.value, 3)),
host.order="",
n.hosts="NA*")
# Return results
return(results)
}
human.sharing.bacteria <- hostShiftingPlotDataHuman("Bacteria")
human.sharing.bacteria$type <- "Bacteria"
human.sharing.virus <- hostShiftingPlotDataHuman("Virus")
human.sharing.virus$type <- "Virus"
plot.df <- rbind(human.sharing.bacteria, human.sharing.virus)
# The parameter starting values for the sigmoidal fit are from fitting the sigmoid the bacteria data only with nls:
# fit <- nls(perc.shared ~ SSlogis(mean.distance, Asym, xmid, scal), data = human.sharing.bacteria)
p.human.sharing <- ggplot(plot.df, aes(x=mean.distance, y=perc.shared))+
geom_point(aes(size=n.hosts, fill=type), colour="black", pch=21, alpha=0.5)+
xlim(c(0,max(plot.df$mean.distance)))+
geom_smooth(method="nls", aes(group=type, colour=type),size=2, formula= y ~ Asym/(1+exp((xmid-x)/scal)), method.args = list(start=c(Asym=0.7, xmid=1.31, scal=-0.33)), se=FALSE)+
scale_fill_manual(values=c("black", "red"))+
scale_colour_manual(values=c("black", "red"))+
scale_size_continuous(range = c(2,10))+
ylim(c(0,1))+
labs(size="Number of\nhost-pathogen\nassociations", fill="Pathogen type", colour="Pathogen type")+
xlab(paste("Mean phylogenetic distance to Homo sapiens"))+
ylab(paste("Fraction of shared pathogens with Homo sapiens"))+
theme_basic()+
theme(axis.text=element_text(size=16),
legend.text =element_text(colour="black", size=16),
legend.title=element_text(colour="black", size=18),
strip.text.x=element_text(colour="black", size=22),
axis.title=element_text(colour="black", size=22))
```
We combine the left and right panels together to make a preliminary version of the figure. The paper version was produced manually from this preliminary version in [Inkscape](https://inkscape.org/), using animal silhouettes from [phylopic.org](https://phylopic.org).
```{r Fig_host_shifting_plot, cache=cachedata}
pdf("figures/Figure-6-pathogen-sharing.pdf", width=17, height=10)
cowplot::plot_grid(p.pathogen.sharing.orders.10+ggtitle("(a)"), p.human.sharing+ggtitle("(b)"))
dev.off()
png("figures/Figure-6-pathogen-sharing.png", width=1700, height=1000, pointsize = 14)
cowplot::plot_grid(p.pathogen.sharing.orders.10, p.human.sharing)
dev.off()
# Show plot
cowplot::plot_grid(p.pathogen.sharing.orders.10, p.human.sharing)
```
It is useful to be able to match the points and names together, which can be done with the plot below.
```{r Fig_host_shifting_plot_match_names, cache=cachedata}
ggplot(plot.df, aes(x=mean.distance, y=perc.shared))+
geom_text(aes(label=host.order, colour=type), nudge_x = 0,nudge_y=0, size=1, angle=45)+
stat_smooth(method="loess", aes(group=type, colour=type),size=2, se = FALSE, formula = y ~ exp(x)/(exp(x)+1))+
xlim(c(0,max(plot.df$mean.distance)))+
scale_fill_manual(values=c("black", "red"))+
scale_colour_manual(values=c("black", "red"))+
scale_size_continuous(range = c(2,10))+
ylim(c(0,1))+
labs(size="Number of\nhost-pathogen\nassociations", fill="Pathogen type", colour="Pathogen type")+
xlab(paste("Mean phylogenetic distance to Homo sapiens"))+
ylab(paste("Fraction of shared pathogens with Homo sapiens"))+
theme_basic()+geom_point(aes(colour=type))+facet_wrap(~type)
ggsave("figures/Figure-6-pathogen-sharing-right-panel-names.pdf")
```
# Supplementary Figures
## Supplementary Figure 1
We compare our mitochondrial gene phylogeny with the *cytb* phylogeny of Olival et al.
```{r tree_comparison_olival, cache=cachedata}
olival.tree <- read.tree('data/olival_cytb_supertree.tree')
# Convert tip labels
olival.tree$tip.label <- gsub("_", "", olival.tree$tip.label)
# Only consider shared species
olival.tree.shared <- drop.tip(olival.tree,
tip=olival.tree$tip.label[which(!olival.tree$tip.label %in% host_tree$tip.label)])
olival.tree.cophenetic <- cophenetic(olival.tree.shared)
this.study.tree.cophenetic <- d[rownames(olival.tree.cophenetic), rownames(olival.tree.cophenetic)]
# Plot cophenetic distances
tree.df <- data.frame(cbind(as.vector(olival.tree.cophenetic), as.vector(this.study.tree.cophenetic)))
p.olival.cophenetic.comparison <- ggplot(tree.df, aes(X1, X2))+
geom_point(size=0.25, alpha=0.1)+
theme_basic()+
xlab("Cophenetic distance (Olival et al. 2017)")+
ylab("Cophenetic distance (this study)")+
geom_abline(linetype='dashed')+
stat_smooth(method="lm", colour="red")
p.olival.cophenetic.comparison
png("figures/Supplementary-Figure-1-Cophenetic-Tree-Comparison.png", width=800, height=1200)
p.olival.cophenetic.comparison
dev.off()
```
## Supplementary Figure 2
Mean and maximum PHB are well-correlated for most pathogens.
```{r Supp_Fig_2, cache=cachedata}
p.mean.max.phb <- ggplot(unique_pathogens, aes(x=PHB, y=PHB.max))+geom_point(aes(size=N.hosts, colour=Type))+theme_basic()+
facet_wrap(~Type)+
scale_color_manual(values=c("black", "red"))+
geom_abline(slope=1, intercept=0)
p.mean.max.phb
pdf("figures/Supplementary-Figure-2-PHB-correlation.pdf", width=12, height=8)
p.mean.max.phb
dev.off()
```
## Supplementary Figure 3
Shows how PHB varies within bacterial families.
```{r Figure_PHB_by_pathogen_family, cache=cachedata}
# Family
min.abundance <- 20
df.generalist.prop.family <- data.frame(bacteria.df %>% group_by(family) %>% summarise(prop.generalist=length(n.hosts[which(n.hosts!=1)])/length(n.hosts), species=length(n.hosts)))