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analyses_and_plots.Rmd
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---
title: "Symbiont diversity - Analyses and plotting"
author: "Anna Mankowski"
output: html_document
---
# Load libraries
```{r Load libraries, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
library("phyloseq")
library("ape")
library("rareNMtests")
library("ggplot2")
library("ggpmisc")
library("vegan")
library("gridExtra")
library("plyr")
library("MASS")
library("gdata")
library("dplyr")
library("reshape2")
library("forcats")
library("patchwork")
library("phytools")
library("igraph")
library("Hmisc")
library("optparse")
library("reshape2")
library("stringr")
library("ggpubr")
library("maps")
library("mapdata")
library("data.table")
library("ade4")
library("tidyverse")
library("spaa")
library("geodist")
```
# Load and format data
```{r Load and format data, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
### Abundance data
# Per host individual
data.matrix<-read.csv("./data/abundance-matrix.tsv", header=T, row.names=1, sep="\t")
data.table<-read.csv("./data/abundances.tsv", sep="\t", header=T)
data.table.gb<-read.csv("./data/abundances.GB.tsv", sep="\t", header=T)
# Averaged per host species
data.matrix.averaged<-read.csv("./data/abundance-matrix-averaged.tsv", sep="\t", header=T, row.names=1)
### Metadata
metadata<-read.csv("./data/metadata.csv", sep=";", header=T)
metadata.maps<-read.csv("./data/metadata.maps.csv", sep=";", header=T)
metadata.phyloseq<-read.csv("./data/metadata.csv", sep=";", header=T, row.names=1)
### Age data
age<-read.csv("./data/results.dating.csv", sep=";", header=T)
### Host and symbiont phylogenies
# Host
host.tree.species<-read.tree("./data/host-tree.species.newick")
host.tree.individuals<-read.tree("./data/host-tree.individuals.newick")
host.tree.treecmp<-read.tree("data/host-tree.individuals.for-TreeCmp.newick")
# Mito
mito.tree.species<-read.tree("./data/mito-tree.species.newick")
mito.tree.individuals<-read.tree("./data/mito-tree.individuals.newick")
mito.tree.treecmp<-read.tree("data/mito-tree.individuals.for-TreeCmp.newick")
# Symbionts
symbiont.tree.clades<-read.tree("./data/symbiont-tree.clades.newick")
symbiont.tree.phyloseq<-drop.tip(symbiont.tree.clades,"clonepJP4116SribosomalRNAgenefragment" )
### Vectors of host species and symbiont clade names in phylogenetic order for data organization
hosts=c(host.tree.species$tip.label[63:64],host.tree.species$tip.label[62],host.tree.species$tip.label[60:61],host.tree.species$tip.label[57],host.tree.species$tip.label[58:59],host.tree.species$tip.label[3],host.tree.species$tip.label[1:2],host.tree.species$tip.label[22:23],host.tree.species$tip.label[21],host.tree.species$tip.label[20],host.tree.species$tip.label[19],host.tree.species$tip.label[4:5],host.tree.species$tip.label[18],host.tree.species$tip.label[17],host.tree.species$tip.label[16],host.tree.species$tip.label[14:15],host.tree.species$tip.label[6:13],host.tree.species$tip.label[27:28],host.tree.species$tip.label[24:26],host.tree.species$tip.label[35],host.tree.species$tip.label[34],host.tree.species$tip.label[32:33],host.tree.species$tip.label[31],host.tree.species$tip.label[29:30],host.tree.species$tip.label[55:56],host.tree.species$tip.label[51:54],host.tree.species$tip.label[50],host.tree.species$tip.label[48:49],host.tree.species$tip.label[40:41],host.tree.species$tip.label[39],host.tree.species$tip.label[38],host.tree.species$tip.label[36:37],host.tree.species$tip.label[47],host.tree.species$tip.label[46],host.tree.species$tip.label[45],host.tree.species$tip.label[44],host.tree.species$tip.label[42:43])
symbionts<-c(symbiont.tree.clades$tip.label[32:33], symbiont.tree.clades$tip.label[31], symbiont.tree.clades$tip.label[30], symbiont.tree.clades$tip.label[26:27], symbiont.tree.clades$tip.label[24], symbiont.tree.clades$tip.label[25], symbiont.tree.clades$tip.label[23], symbiont.tree.clades$tip.label[22], symbiont.tree.clades$tip.label[28:29], symbiont.tree.clades$tip.label[20:21], symbiont.tree.clades$tip.label[17:19], symbiont.tree.clades$tip.label[16], symbiont.tree.clades$tip.label[15], symbiont.tree.clades$tip.label[13:14], symbiont.tree.clades$tip.label[7:12], symbiont.tree.clades$tip.label[2:6], symbiont.tree.clades$tip.label[1])
### Merge and sort abundance data and metadata
data.table.maps<-merge(data.table, metadata.maps, by="lib")
data.table.metadata<-merge(data.table, metadata, by ="lib")
data.table.metadata$host_species<-reorder.factor(data.table.metadata$host_species, new.order=hosts)
data.table.metadata %>% arrange(host_species)
data.table.metadata$symbiont<-as.character(data.table.metadata$symbiont)
data.table.metadata$symbiont<-factor(data.table.metadata$symbiont, levels=symbionts)
### Generate PhyloSeq objects
# Using symbiont abundances of host individuals
otu<-t(data.matrix)
OTU<-otu_table(otu, taxa_are_rows=T)
sampledata<-sample_data(metadata.phyloseq)
physeq<-phyloseq(OTU, sampledata, symbiont.tree.phyloseq)
# Using averaged symbiont abundances of host species
otu.av<-t(data.matrix.averaged)
OTU.av<-otu_table(otu.av, taxa_are_rows=T)
physeq.av<-phyloseq(OTU.av, symbiont.tree.phyloseq)
```
```{r Set colors echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
symbiont.colors<-c("Alpha1"="#660000", "Alpha2"="#FF6633", "Alpha3"="#993300", "Alpha4"="#FF9999",
"Alpha5"="#CC3300", "Alpha6"="#FF9966", "Alpha7"="#CC0066", "Alpha8"="#FF0066",
"Alpha9"="#FF0033", "Alpha10"="#FF6600", "Alpha12"="#993333", "Alpha13"="#FF9900",
"Alpha14"="#FFCC99", "Alpha15"="#FF3333", "Alpha16"="#FF3300", "Delta1"="#6699FF",
"Delta2"="#CCFFFF", "Delta3"="#00FFFF", "Delta4"="#00CCFF", "Delta5"="#006699",
"Delta11"="#669999","Delta12"="#0099CC", "Delta13"="#000066", "Delta14"="#660099",
"Gamma1"="#339933", "Gamma2"="#00FF00", "Gamma3"="#336633", "Gamma4"="#99FF99",
"Gamma5"="#009966", "Gamma7"="#999900", "Spiro1"="#FF00FF", "Actinomarinales1"="#FFFF66",
"Marinimicrobia1"="#FFFF99")
symbiont.colors.age<-c(symbiont.colors, "host"="gray")
host.colors<-c("Iaduncosetis"="dodgerblue4", "IdutchaeA"="cadetblue3", "IdutchaeB"="cadetblue2",
"Iexumae"="dodgerblue2", "Ileukodermatus"="paleturquoise4", "Imakropetalos"="paleturquoise",
"Imanae"="deepskyblue3", "Imojicae"="paleturquoise3", "Ireginae"="cadetblue",
"Iscalprum"="dodgerblue3", "IspFANT"="deepskyblue2", "Ispguadeloupe1"="paleturquoise1",
"IspNYSP"="paleturquoise2", "IspULE"="dodgerblue", "Itriangulatus"="cadetblue4",
"Iwasseri"="royalblue4", "Oalbidus"="olivedrab", "Oalgarvensis"="olivedrab4",
"Onravisceralis"="mediumorchid", "Oclavatus"="seagreen1",
"Ocrassitunicatus"="tomato1", "Ofaris"="hotpink3", "Ofilicauda"="tomato",
"Ofilithecatus"="darkseagreen2", "Ofinitimus"="orangered4", "Ofurinus"="firebrick1",
"Ogeniculatus"="aquamarine3", "Oilvae"="tomato2", "Oimperfectus"="violetred4",
"Onrloisae"="mediumorchid4", "OlongissimusB"="mediumpurple4", "Onivalis"="seagreen3",
"Onralgarvensis"="mediumpurple3", "Onrfaris"="hotpink1", "Onrtenuissimus"="firebrick2",
"Oprodigus"="aquamarine4", "Osp11"="firebrick4", "Osp66"="seagreen2",
"Ospbahamas1"="violetred", "Ospbelize1"="tomato3", "OspCAPE"="darkseagreen1",
"Ospdahab1"="royalblue3", "OspDARISL"="darkred", "OspFALSE"="darkseagreen4",
"OspFILI"="brown2", "OspHEAD"="brown4", "Ospheron1"="springgreen3", "Ospheron3"="seagreen",
"Ospheron4"="aquamarine", "Ospheron5"="seagreen4", "OspLEES"="violetred1",
"Ospnorthcarolina1"="violetred3", "Ospoahu1"="firebrick3", "Ospokinawa1"="darkseagreen3",
"Ospokinawa2"="olivedrab2", "OspOTHER"="lightpink", "Ospcanary1"="royalblue1",
"OspTT"="olivedrab3", "OspWHICH"="brown3", "Otantulus"="tomato4", "Otenuissimus"="firebrick",
"Oullae"="brown", "Ovacuus"="brown1", "Ozimmermannae"="violetred2")
ocean.colors<-c("Atlantic"="indianred1",
"Mediterranean"="royalblue",
"RedSea"="lightgoldenrod1",
"Indian"="mediumpurple3",
"Pacific"="seagreen3")
country.colors<-c("WesternAustralia"="mediumpurple3",
"Queensland"="springgreen",
"Bahamas"="indianred",
"Belize"="indianred1",
"Bermuda"="indianred3",
"Egypt"="lightgoldenrod",
"Italy"="royalblue",
"France"="royalblue4",
"Guadeloupe"="indianred4",
"Hawaii"="palegreen",
"Spain"="skyblue3",
"NewCaledonia"="palegreen4",
"NorthCarolina"="tomato3",
"Japan"="seagreen3",
"Peru"="palegreen3",
"SouthAfrica"="firebrick1",
"Canaries"="salmon1")
organic.colors<-c("a_unknown"="gainsboro",
"unspecified"="gray",
"algal_crust"="darkseagreen1",
"algal_plumbs"="darkseagreen2",
"corals"="coral",
"corals_algae"="coral3",
"seagrass"="darkseagreen4",
"seagrass_algal_plumbs"="springgreen4",
"seagrass_corals"="seagreen4",
"seagrass_detritus"="seagreen3",
"detritus"="sienna4")
sedtype.colors<-c("a_unknown"="gainsboro",
"sand"="lightgoldenrod",
"carbonate_sand"="lightsalmon3",
"coral_sand"="coral",
"silicate_sand"="ivory",
"mud_sand"="tan",
"mud"="tan3",
"silt"="sienna4")
```
# Analyze and plot
```{r Compare congruence between host trees echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
shared.nodes.hosts<-matchNodes(host.tree.individuals, mito.tree.individuals, method="descendants")[,2]
length(shared.nodes.hosts)
shared.nodes.hosts<-sum(!is.na(shared.nodes.hosts))
shared.nodes.hosts
```
```{r Rarefaction echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
data.rare.q0<-rarefaction.sample(data.matrix)
data.rare.q1<-rarefaction.sample(data.matrix, q=1)
data.rare.q2<-rarefaction.sample(data.matrix, q=2)
data.rare<-merge(data.rare.q0, data.rare.q1, by="sample-size")
data.rare<-merge(data.rare, data.rare.q2, by="sample-size")
names(data.rare)<-c("samples", "q0", "q1", "q2")
data.rare<-data.rare %>% select(samples, q0, q1, q2) %>% gather(key="variable", value="value", -samples)
rare.plot<-ggplot(data.rare) +
geom_point(aes(x=samples, y=value, colour=variable)) +
scale_color_manual(values=c("q2"="darkseagreen1", "q0"="springgreen4","q1"="seagreen3"),
labels=c("Clade number", "Shannon index", "Inverse Simpson index"),
name="") +
theme_minimal() +
theme(axis.text.x=element_text(size=12)) +
theme(axis.text.y=element_text(size=12)) +
theme(legend.position="top") +
labs(x="Number of host individuals", y="Symbiont clade richness")
rare.plot
ggsave(rare.plot, file="./plots/rare.plot.eps", height=200, width=200, units="mm")
```
```{r Plot sampling locations, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
world<-map_data("world")
map.sampling.locations<-ggplot() +
geom_polygon(data=world, aes(x=long, y=lat, group=group) , fill="grey")+
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
panel.border=element_blank()) +
theme(axis.title=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank()) +
geom_point(data=data.table.maps, aes(x=longitude, y=latitude, colour=country), size=3) +
scale_colour_manual(values=country.colors, name="Country") +
theme(legend.position="none")
map.sampling.locations
ggsave(map.sampling.locations, file="./plots/map.sampling.locations.eps", width=200, height=100, units="mm", limitsize=F)
```
```{r Plot abundances , echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
abundance.plot<-ggplot(data.table.metadata, aes(fct_rev(symbiont), fct_rev(host_species))) +
geom_point(aes(size=abundance, fill=symbiont), colour="black", pch=21) +
scale_size_continuous(range=c(0,6),
guide="legend",
name="Relative abundance (%)") +
scale_fill_manual(values=symbiont.colors,
breaks=c("Alpha1", "Alpha2", "Alpha3", "Alpha4", "Alpha5", "Alpha6", "Alpha7",
"Alpha8", "Alpha9", "Alpha10", "Alpha12", "Alpha13", "Alpha14", "Alpha15",
"Alpha16"," Delta1", "Delta2", "Delta3", "Delta4", "Delta5", "Delta8",
"Delta11", "Delta12", "Delta13", "Delta14", "Gamma1", "Gamma2", "Gamma3",
"Gamma4", "Gamma4a", "Gamma5", "Gamma6", "Gamma7", "Gamma8",
"Spiro1", "Actinomarinales1", "Marinimicrobia1"),
guide=FALSE) +
theme_minimal() +
#theme(panel.grid.major=element_line(colour="#CCCCCC")) +
theme(legend.position="bottom") +
scale_x_discrete(position="top") +
#theme(text=element_text(family="Arial")) +
theme(axis.text.x=element_text(angle=-90, hjust=1, size=5),
axis.title.x=element_blank()) +
theme(axis.text.y=element_text(size=5),
axis.title.y=element_blank())
gb.abundance.plot<-ggplot(data.table.gb, aes(symbiont, fct_rev(lib))) +
geom_point(aes(size=abundance, fill=symbiont), colour="black", pch=21) +
scale_size_continuous(range=c(0,6),
guide="legend",
name="Relative abundance (%)") +
scale_fill_manual(values=symbiont.colors,
breaks=c("Alpha1", "Alpha2", "Alpha3", "Alpha4", "Alpha5", "Alpha6", "Alpha7",
"Alpha8", "Alpha9", "Alpha10", "Alpha12", "Alpha13", "Alpha14", "Alpha15",
"Alpha16"," Delta1", "Delta2", "Delta3", "Delta4", "Delta5", "Delta8",
"Delta11", "Delta12", "Delta13", "Delta14", "Gamma1", "Gamma2", "Gamma3",
"Gamma4", "Gamma4a", "Gamma5", "Gamma6", "Gamma7", "Gamma8",
"Spiro1", "Actinomarinales1", "Marinimicrobia1"),
guide=FALSE) +
theme_minimal() +
theme(legend.position="bottom") +
scale_x_discrete(position="top") +
theme(axis.text.x=element_text(angle=-90, hjust=1),
axis.title.x=element_blank()) +
theme(axis.title.y=element_blank()) +
theme(text=element_text(size=5))
abundance.plot
gb.abundance.plot
ggsave(abundance.plot, file="./plots/abundance.plot.eps", width=200, height=200, units="mm", limitsize=F)
ggsave(gb.abundance.plot, file="./plots/gb.abundance.plot.eps", width=180, height=60, units="mm")
```
```{r Generate UniFrac distances, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
# Create distance matrix with all samples
set.seed(0)
unifrac.dist<-ordinate(physeq=physeq, method="NMDS", distance="unifrac")
NMDS1<-as.numeric(unifrac.dist$points[,1])
NMDS2<-as.numeric(unifrac.dist$points[,2])
NMDS1<-as.data.frame(x=NMDS1, row.names=rownames(data.matrix))
NMDS2<-as.data.frame(x=NMDS2, row.names=rownames(data.matrix))
df.unifrac<-merge(NMDS1, NMDS2, by=0, all=T)
rownames(df.unifrac)<-df.unifrac$Row.names
df.unifrac[,1]<-NULL
df.unifrac<-merge(df.unifrac, metadata, by.x=0, by.y="lib", all=T)
rownames(df.unifrac)<-df.unifrac$Row.names
df.unifrac[,1]<-NULL
df.unifrac$lib<-row.names(df.unifrac)
df.unifrac<-df.unifrac[ which(df.unifrac$NMDS1 != "NA"), ]
```
```{r UniFrac PERMANOVA stats + Mantel test for geographic distance, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
set.seed(0)
### Prepare UniFrac dist object
unifrac<-UniFrac(physeq, F, T, F, T)
unifrac.matrix<-as.matrix(unifrac)
### PERMANOVA
adonis.host_species<-adonis2(distance(physeq, method="unifrac") ~ host_species, data=metadata, permutations=999)
adonis.ocean<-adonis2(distance(physeq, method="unifrac") ~ ocean, data=metadata, permutations=999)
adonis.organic_input<-adonis2(distance(physeq, method="unifrac") ~ organic_input, data=metadata, permutations=999)
adonis.sediment_type<-adonis2(distance(physeq, method="unifrac") ~ sediment_type, data=metadata, permutations=999)
### Mantel test
# Generate geographic distance matrix
geodist<-metadata[, c(1,15,16)]
geodist<-geodist[ which(geodist$latitude != "a_unknown"),]
geodist.matrix<-geodist::geodist(geodist)
row.names(geodist.matrix)<-geodist$lib
colnames(geodist.matrix)<-geodist$lib
geo.dist<-as.dist(geodist.matrix, upper=T, diag=T)
# Subset UniFrac distance matrix
geo<-row.names(geodist.matrix)
unifrac.forgeo<-unifrac.matrix[(row.names(unifrac.matrix) %in% geo),]
unifrac.forgeo<-unifrac.forgeo[,(colnames(unifrac.forgeo) %in% geo)]
unifrac.forgeo.dist<-as.dist(unifrac.forgeo, upper=T, diag=T)
mantel.unifrac.geo<-mantel.rtest(unifrac.forgeo.dist, geo.dist, nrepet=999)
adonis.host_species
adonis.ocean
adonis.organic_input
adonis.sediment_type
mantel.unifrac.geo
```
```{r UniFrac NMDS with different colorations for different parameters, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
### Base and blank plots
unifrac.nmds.base<-ggplot(data=df.unifrac) +
theme_minimal() +
theme(axis.text=element_text(size=12)) +
theme(axis.title=element_text(size=12)) +
theme(axis.text.x=element_text(size=12),
axis.text=element_text(size=12)) +
labs(y="NMDS2", x="NMDS1") +
theme(legend.text=element_text(size=12))
unifrac.nmds.blank<-ggplot(data=df.unifrac) +
geom_point(aes(NMDS1, NMDS2), colour="white", alpha=0) + theme_minimal() +
theme(axis.title=element_text(color="white"),
axis.text=element_text(color="white"),
axis.ticks=element_blank(),
panel.grid=element_blank())
### Oceans
oc<-c(levels(df.unifrac$ocean)[1], levels(df.unifrac$ocean)[3], levels(df.unifrac$ocean)[5], levels(df.unifrac$ocean)[2], levels(df.unifrac$ocean)[4])
df.unifrac<-left_join(data.frame(ocean=oc), df.unifrac, by="ocean")
unifrac.nmds.ocean<-unifrac.nmds.base +
geom_point(data=df.unifrac, aes(NMDS1, NMDS2, colour=ocean), size=10) +
scale_colour_manual(
values=ocean.colors,
breaks=oc) +
guides(colour=guide_legend("Ocean"))
### Organic input
organic<-c(levels(df.unifrac$organic_input)[1], levels(df.unifrac$organic_input)[2:5], levels(df.unifrac$organic_input)[7:10], levels(df.unifrac$organic_input)[6])
df.unifrac<-left_join(data.frame(organic_input=organic), df.unifrac, by="organic_input")
unifrac.nmds.organic<-unifrac.nmds.base +
geom_point(data=df.unifrac, aes(NMDS1, NMDS2, colour=organic_input), size=10) +
scale_colour_manual(
values=organic.colors,
breaks=organic) +
guides(colour=guide_legend("Organic input"))
### Sediment type
sed_type<-c(levels(df.unifrac$sediment_type)[1], levels(df.unifrac$sediment_type)[6], levels(df.unifrac$sediment_type)[2:3], levels(df.unifrac$sediment_type)[7], levels(df.unifrac$sediment_type)[5], levels(df.unifrac$sediment_type)[4], levels(df.unifrac$sediment_type)[8])
df.unifrac<-left_join(data.frame(sediment_type=sed_type), df.unifrac, by="sediment_type")
unifrac.nmds.sedimenttype<-unifrac.nmds.base +
geom_point(data=df.unifrac, aes(NMDS1, NMDS2, colour=sediment_type), size=10) +
scale_colour_manual(
values=sedtype.colors,
breaks=sed_type) +
guides(colour=guide_legend("Sediment type"))
### Combined plots
unifrac.nmds.final<-(unifrac.nmds.ocean | unifrac.nmds.sedimenttype | unifrac.nmds.organic)
unifrac.nmds.final
ggsave(unifrac.nmds.final, file="./plots/unifrac.nmds.final.eps", width=600, height=200, units="mm", limitsize=F)
```
```{r Prepare congruence analyses between host species and species-level symbiont community composition, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
set.seed(0)
### Generate dendrogramm reflecting dissimilarities of symbiont community composition
unifrac.av<-UniFrac(physeq.av, F, T, F, T)
unifrac.av.clust<-hclust(as.dist(unifrac.av))
unifrac.av.dendro<-as.phylo(unifrac.av.clust)
write.tree(phy=unifrac.av.dendro, file="./analyses/TreeCmp/mt.newick")
### Generate random trees
random.trees<-rmtree(100000, 64, rooted=T, tip.label=hosts)
write.tree(phy=random.trees, file="./analyses/TreeCmp/random_trees/random_trees.newick")
### Prune and save host trees in analyses directory
remove.host<-setdiff(host.tree.treecmp$tip.label, hosts)
host.tree.treecmp.final<-drop.tip(host.tree.treecmp, remove.host)
write.tree(phy=host.tree.treecmp.final, file="./analyses/TreeCmp/host.newick")
### Prune and save mito tree in analyses directory
remove.host<-setdiff(host.tree.treecmp$tip.label, hosts)
host.tree.treecmp.final<-drop.tip(host.tree.treecmp, remove.host)
write.tree(phy=host.tree.treecmp.final, file="./analyses/TreeCmp/mito.newick")
```
```{bash Calculate congruence, echo=TRUE}
# Needs to be executed directly on the command line, python scripts request direct input
cd ./analyses/TreeCmp
# Congruence of host phylogeny vs. symbiont community composition
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r host.newick -d rf -i mt.newick -o host.treecmp_rf.txt -P -N
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r host.newick -d rf -i random_trees/random_trees.newick -o host.treecmp_stochastic_rf.txt -P -N
python ../../scripts/pandas_collate_robinson_foulds_rf.py -i ./host.treecmp_stochastic_rf.txt,./host.treecmp_rf.txt
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r host.newick -d mc -i mt.newick -o host.treecmp_mc.txt -P -N
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r host.newick -d mc -i random_trees/random_trees.newick -o host.treecmp_stochastic_mc.txt -P -N
python ../../scripts/pandas_collate_robinson_foulds_mc.py -i ./host.treecmp_stochastic_mc.txt,./host.treecmp_mc.txt
# Congruence of mito phylogeny vs. symbiont community composition
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r mito.newick -d rf -i mt.newick -o mito.treecmp_rf.txt -P -N
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r mito.newick -d rf -i random_trees/random_trees.newick -o mito.treecmp_stochastic_rf.txt -P -N
python ../../scripts/pandas_collate_robinson_foulds_rf.py -i ./mito.treecmp_stochastic_rf.txt,./mito.treecmp_rf.txt
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r mito.newick -d mc -i mt.newick -o mito.treecmp_mc.txt -P -N
java -jar /Users/amankows/software/TreeCmp/bin/TreeCmp.jar -r mito.newick -d mc -i random_trees/random_trees.newick -o mito.treecmp_stochastic_mc.txt -P -N
python ../../scripts/pandas_collate_robinson_foulds_mc.py -i ./mito.treecmp_stochastic_mc.txt,./mito.treecmp_mc.txt
```
```{bash TreeCmp results, eval=FALSE}
Host RF
Summary: Stochatic Comparison
Number Columns: 7
Number Indices: 100000
Summary: Host-Microbe Comparison
Number Columns: 7
Number Indices: 1
Host-Microbe Score: 53.5
Better Score: 0
Worse Score: 100000
Equiv Score: 0
P-value better: 0.0
Better\Equal Score: 0
Worse Score: 100000
P-value Better/Equal: 0.0
Max Stochastic Metric: 60.5
Host MC
Summary: Stochatic Comparison
Number Columns: 7
Number Indices: 100000
Summary: Host-Microbe Comparison
Number Columns: 7
Number Indices: 1
Host-Microbe Score: 486.0
Better Score: 1765
Worse Score: 98087
Equiv Score: 148
P-value better: 0.01765
Better\Equal Score: 1913
Worse Score: 98087
P-value Better/Equal: 0.01913
Max Stochastic Metric: 630.0
Mito RF
Summary: Stochatic Comparison
Number Columns: 7
Number Indices: 100000
Summary: Host-Microbe Comparison
Number Columns: 7
Number Indices: 1
Host-Microbe Score: 53.5
Better Score: 0
Worse Score: 100000
Equiv Score: 0
P-value better: 0.0
Better\Equal Score: 0
Worse Score: 100000
P-value Better/Equal: 0.0
Max Stochastic Metric: 60.5
Mito MC
Summary: Stochatic Comparison
Number Columns: 7
Number Indices: 100000
Summary: Host-Microbe Comparison
Number Columns: 7
Number Indices: 1
Host-Microbe Score: 486.0
Better Score: 1765
Worse Score: 98087
Equiv Score: 148
P-value better: 0.01765
Better\Equal Score: 1913
Worse Score: 98087
P-value Better/Equal: 0.01913
Max Stochastic Metric: 630.0
```
```{r Calculate number of shared nodes between host phylogenies and permuted symbiont community compositions, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
shared.nodes.host<-matchNodes(host.tree.species, unifrac.av.dendro, method="descendants")[,2]
shared.nodes.host<-sum(!is.na(shared.nodes.host))
shared.nodes.host.perm<-vector()
data.matrix.averaged.t<-t(data.matrix.averaged)
for (i in 1:1000){
colnames(data.matrix.averaged.t)<-sample(colnames(data.matrix.averaged.t))
otu.perm<-data.matrix.averaged.t
OTU.perm<-otu_table(otu.perm, taxa_are_rows=T)
physeq.perm<-phyloseq(OTU.perm, symbiont.tree.phyloseq)
unifrac.perm<-UniFrac(physeq.perm, F, T, F, T)
unifrac.perm.clust<-hclust(as.dist(unifrac.perm))
unifrac.perm.dendro<-as.phylo(unifrac.perm.clust)
sample<-matchNodes(host.tree.species, unifrac.perm.dendro, method="descendants")[,2]
shared.nodes.host.perm<-c(shared.nodes.host.perm, sum(!is.na(sample)))
}
shared.nodes.host.fused<-c(shared.nodes.host.perm, shared.nodes.host)
shared.nodes.host.fused<-as.data.frame(shared.nodes.host.fused-1)
shared.nodes.host.df<-as.data.frame(shared.nodes.host.perm-1)
colnames(shared.nodes.host.df)<-"x"
shared.nodes.host=shared.nodes.host-1
shared.nodes.host.ttest<-t.test(shared.nodes.host.df$x, mu=shared.nodes.host)
shared.nodes.mito<-matchNodes(mito.tree.species, unifrac.av.dendro, method="descendants")[,2]
shared.nodes.mito<-sum(!is.na(shared.nodes.mito))
shared.nodes.mito.perm<-vector()
data.matrix.averaged.t<-t(data.matrix.averaged)
for (i in 1:1000){
colnames(data.matrix.averaged.t)<-sample(colnames(data.matrix.averaged.t))
otu.perm<-data.matrix.averaged.t
OTU.perm<-otu_table(otu.perm, taxa_are_rows=T)
physeq.perm<-phyloseq(OTU.perm, symbiont.tree.phyloseq)
unifrac.perm<-UniFrac(physeq.perm, F, T, F, T)
unifrac.perm.clust<-hclust(as.dist(unifrac.perm))
unifrac.perm.dendro<-as.phylo(unifrac.perm.clust)
sample<-matchNodes(mito.tree.species, unifrac.perm.dendro, method="descendants")[,2]
shared.nodes.mito.perm<-c(shared.nodes.mito.perm, sum(!is.na(sample)))
}
shared.nodes.mito.fused<-c(shared.nodes.mito.perm, shared.nodes.mito)
shared.nodes.mito.fused<-as.data.frame(shared.nodes.mito.fused-1)
shared.nodes.mito.df<-as.data.frame(shared.nodes.mito.perm-1)
colnames(shared.nodes.mito.df)<-"x"
shared.nodes.mito=shared.nodes.mito-1
shared.nodes.mito.ttest<-t.test(shared.nodes.mito.df$x, mu=shared.nodes.mito)
```
```{r Test whether sister species with similar communities are more closely related than other sister species, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
host.individuals.phylo.matrix<-cophenetic(host.tree.individuals)
host.individuals.phylo.melted<-melt(host.individuals.phylo.matrix)
tmp<-host.individuals.phylo.melted[grep("Oimperfectus", host.individuals.phylo.melted$Var1), ]
H1<-tmp[grep("Ospnorthcarolina1", tmp$Var2), ]
H1$Var1<-"H1"
H1$Var2<-"H1"
tmp<-host.individuals.phylo.melted[grep("Iscalprum", host.individuals.phylo.melted$Var1), ]
H2<-tmp[grep("Iaduncosetis", tmp$Var2), ]
H2$Var1<-"H2"
H2$Var2<-"H2"
tmp<-host.individuals.phylo.melted[grep("IdutchaeA", host.individuals.phylo.melted$Var1), ]
H3<-tmp[grep("IdutchaeB", tmp$Var2), ]
H3$Var1<-"H3"
H3$Var2<-"H3"
tmp<-host.individuals.phylo.melted[grep("Ogeniculatus", host.individuals.phylo.melted$Var1), ]
H4<-tmp[grep("Ospheron4", tmp$Var2), ]
H4$Var1<-"H4"
H4$Var2<-"H4"
tmp<-host.individuals.phylo.melted[grep("Ospheron5", host.individuals.phylo.melted$Var1), ]
H5<-tmp[grep("Ospheron3", tmp$Var2), ]
H5$Var1<-"H5"
H5$Var2<-"H5"
tmp<-host.individuals.phylo.melted[grep("Onivalis", host.individuals.phylo.melted$Var1), ]
H6<-tmp[grep("Osp66", tmp$Var2), ]
H6$Var1<-"H6"
H6$Var2<-"H6"
tmp<-host.individuals.phylo.melted[grep("OspLEES", host.individuals.phylo.melted$Var1), ]
H7<-tmp[grep("Ospbahamas1", tmp$Var2), ]
H7$Var1<-"H7"
H7$Var2<-"H7"
tmp<-host.individuals.phylo.melted[grep("Oullae", host.individuals.phylo.melted$Var1), ]
H8<-tmp[grep("Ovacuus", tmp$Var2), ]
H8$Var1<-"H8"
H8$Var2<-"H8"
tmp<-host.individuals.phylo.melted[grep("Oalgarvensis", host.individuals.phylo.melted$Var1), ]
HR1<-tmp[grep("nralgarvensis", tmp$Var2), ]
HR1$Var2<-"HR1"
tmp<-host.individuals.phylo.melted[grep("Oilvae", host.individuals.phylo.melted$Var1), ]
HR2<-tmp[grep("OspFALSE", tmp$Var2), ]
HR2$Var2<-"HR2"
tmp<-host.individuals.phylo.melted[grep("IspFANT", host.individuals.phylo.melted$Var1), ]
HR3<-tmp[grep("Imanae", tmp$Var2), ]
HR3$Var2<-"HR3"
tmp<-host.individuals.phylo.melted[grep("Ileukodermatus", host.individuals.phylo.melted$Var1), ]
HR4<-tmp[grep("Imojicae", tmp$Var2), ]
HR4$Var2<-"HR4"
tmp<-host.individuals.phylo.melted[grep("Iexumae", host.individuals.phylo.melted$Var1), ]
HR5<-tmp[grep("IspULE", tmp$Var2), ]
HR5$Var2<-"HR5"
tmp<-host.individuals.phylo.melted[grep("Ireginae", host.individuals.phylo.melted$Var1), ]
HR6<-tmp[grep("triangulatus", tmp$Var2), ]
HR6$Var2<-"HR6"
tmp<-host.individuals.phylo.melted[grep("Ospokinawa2", host.individuals.phylo.melted$Var1), ]
HR7<-tmp[grep("OspTT", tmp$Var2), ]
HR7$Var2<-"HR7"
tmp<-host.individuals.phylo.melted[grep("Ocrassitunicatus", host.individuals.phylo.melted$Var1), ]
HR8<-tmp[grep("Ofilicauda", tmp$Var2), ]
HR8$Var2<-"HR8"
tmp<-host.individuals.phylo.melted[grep("Otantulus", host.individuals.phylo.melted$Var1), ]
HR9<-tmp[grep("Ospbelize1", tmp$Var2), ]
HR9$Var2<-"HR9"
tmp<-host.individuals.phylo.melted[grep("Ospokinawa1", host.individuals.phylo.melted$Var1), ]
HR10<-tmp[grep("Ofilithecatus", tmp$Var2), ]
HR10$Var2<-"HR10"
tmp<-host.individuals.phylo.melted[grep("OspDARISL", host.individuals.phylo.melted$Var1), ]
HR11<-tmp[grep("OspHEAD", tmp$Var2), ]
HR11$Var2<-"HR11"
tmp<-host.individuals.phylo.melted[grep("Ofinitimus", host.individuals.phylo.melted$Var1), ]
HR12<-tmp[grep("Osp11", tmp$Var2), ]
HR12$Var2<-"HR12"
tmp<-host.individuals.phylo.melted[grep("Ofaris", host.individuals.phylo.melted$Var1), ]
HR13<-tmp[grep("Onrfaris", tmp$Var2), ]
HR13$Var2<-"HR13"
tmp<-host.individuals.phylo.melted[grep("Onrloisae", host.individuals.phylo.melted$Var1), ]
HR14<-tmp[grep("Onravisceralis", tmp$Var2), ]
HR14$Var2<-"HR14"
REF<-rbind(HR1, HR2, HR3, HR4, HR5, HR6, HR7, HR8, HR9, HR10, HR11, HR12, HR13, HR14)
REF$Var1<-"REF"
host.phylodist.pairs.final<-rbind(H1, H2, H3, H4, H5, H6, H7, H8, REF)
host.phylodist.pairs.final.ref<-host.phylodist.pairs.final[grep("REF", host.phylodist.pairs.final$Var1), ]$value
host.phylodist.pairs.final.notref<-host.final[grep("REF", host.phylodist.pairs.final$Var1, invert=T), ]$value
host.wilcoxon<-wilcox.test(host.phylodist.pairs.final.ref, host.phylodist.pairs.final.notref, paired=F)
mito.individuals.phylo.matrix<-cophenetic(mito.tree.individuals)
mito.individuals.phylo.melted<-melt(mito.individuals.phylo.matrix)
tmp<-mito.individuals.phylo.melted[grep("Oimperfectus", mito.individuals.phylo.melted$Var1), ]
M1<-tmp[grep("Ospnorthcarolina1", tmp$Var2), ]
M1$Var1<-"M1"
M1$Var2<-"M1"
tmp<-mito.individuals.phylo.melted[grep("Iscalprum", mito.individuals.phylo.melted$Var1), ]
M2<-tmp[grep("Iaduncosetis", tmp$Var2), ]
M2$Var1<-"M2"
M2$Var2<-"M2"
tmp<-mito.individuals.phylo.melted[grep("IdutchaeA", mito.individuals.phylo.melted$Var1), ]
M3<-tmp[grep("IdutchaeB", tmp$Var2), ]
M3$Var1<-"M3"
M3$Var2<-"M3"
tmp<-mito.individuals.phylo.melted[grep("Ogeniculatus", mito.individuals.phylo.melted$Var1), ]
M4<-tmp[grep("Ospheron4", tmp$Var2), ]
M4$Var1<-"M4"
M4$Var2<-"M4"
tmp<-mito.individuals.phylo.melted[grep("Ofinitimus", mito.individuals.phylo.melted$Var1), ]
M5<-tmp[grep("Onrtenuissimus", tmp$Var2), ]
M5$Var1<-"M5"
M5$Var2<-"M5"
tmp<-mito.individuals.phylo.melted[grep("Onivalis", mito.individuals.phylo.melted$Var1), ]
M6<-tmp[grep("Osp66", tmp$Var2), ]
M6$Var1<-"M6"
M6$Var2<-"M6"
tmp<-mito.individuals.phylo.melted[grep("OspLEES", mito.individuals.phylo.melted$Var1), ]
M7<-tmp[grep("Ospbahamas1", tmp$Var2), ]
M7$Var1<-"M7"
M7$Var2<-"M7"
tmp<-mito.individuals.phylo.melted[grep("Oullae", mito.individuals.phylo.melted$Var1), ]
M8<-tmp[grep("Ovacuus", tmp$Var2), ]
M8$Var1<-"M8"
M8$Var2<-"M8"
tmp<-mito.individuals.phylo.melted[grep("Oalgarvensis", mito.individuals.phylo.melted$Var1), ]
MR1<-tmp[grep("Onralgarvensis", tmp$Var2), ]
MR1$Var2<-"MR1"
tmp<-mito.individuals.phylo.melted[grep("Ocrassitunicatus", mito.individuals.phylo.melted$Var1), ]
MR2<-tmp[grep("OspOTHER", tmp$Var2), ]
MR2$Var2<-"MR2"
tmp<-mito.individuals.phylo.melted[grep("Imakropetalos", mito.individuals.phylo.melted$Var1), ]
MR3<-tmp[grep("Imanae", tmp$Var2), ]
MR3$Var2<-"MR3"
tmp<-mito.individuals.phylo.melted[grep("IspNYSP", mito.individuals.phylo.melted$Var1), ]
MR4<-tmp[grep("Imojicae", tmp$Var2), ]
MR4$Var2<-"MR4"
tmp<-mito.individuals.phylo.melted[grep("Otenuissimus", mito.individuals.phylo.melted$Var1), ]
MR5<-tmp[grep("Ofurinus", tmp$Var2), ]
MR5$Var2<-"MR5"
tmp<-mito.individuals.phylo.melted[grep("Ireginae", mito.individuals.phylo.melted$Var1), ]
MR6<-tmp[grep("Itriangulatus", tmp$Var2), ]
MR6$Var2<-"MR6"
tmp<-mito.individuals.phylo.melted[grep("Ospokinawa2", mito.individuals.phylo.melted$Var1), ]
MR7<-tmp[grep("OspTT", tmp$Var2), ]
MR7$Var2<-"MR7"
tmp<-mito.individuals.phylo.melted[grep("Oclavatus", mito.individuals.phylo.melted$Var1), ]
MR8<-tmp[grep("Osp5", tmp$Var2), ]
MR8$Var2<-"MR8"
tmp<-mito.individuals.phylo.melted[grep("Otantulus", mito.individuals.phylo.melted$Var1), ]
MR9<-tmp[grep("Ospbelize1", tmp$Var2), ]
MR9$Var2<-"MR9"
tmp<-mito.individuals.phylo.melted[grep("Ospokinawa1", mito.individuals.phylo.melted$Var1), ]
MR10<-tmp[grep("Ofilithecatus", tmp$Var2), ]
MR10$Var2<-"MR10"
tmp<-mito.individuals.phylo.melted[grep("Oprodigus", mito.individuals.phylo.melted$Var1), ]
MR11<-tmp[grep("Oalbidus", tmp$Var2), ]
MR11$Var2<-"MR11"
tmp<-mito.individuals.phylo.melted[grep("Onrloisae", mito.individuals.phylo.melted$Var1), ]
MR12<-tmp[grep("Onravisceralis", tmp$Var2), ]
MR12$Var2<-"MR12"
tmp<-mito.individuals.phylo.melted[grep("Ofaris", mito.individuals.phylo.melted$Var1), ]
MR13<-tmp[grep("Onrfaris", tmp$Var2), ]
MR13$Var2<-"MR13"
REF<-rbind(MR1, MR2, MR3, MR4, MR5, MR6, MR7, MR8, MR9, MR10, MR11, MR12, MR13)
REF$Var1<-"REF"
mito.phylodist.pairs.final<-rbind(M1, M2, M3, M4, M5, M6, M7, M8, REF)
mito.phylodist.pairs.final.ref<-mito.phylodist.pairs.final[grep("REF", mito.phylodist.pairs.final$Var1), ]$value
mito.phylodist.pairs.final.notref<-mito.phylodist.pairs.final[grep("REF", mito.phylodist.pairs.final$Var1, invert=T), ]$value
mito.wilcoxon<-wilcox.test(mito.phylodist.pairs.final.ref, mito.phylodist.pairs.final.notref, paired=F)
```
```{r Network of symbiont co-occurences using Spearman's correlations, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
cooccur.symbiont<-as.matrix(data.matrix)
# correlation analysis based on spearman's co-efficient
cooccur.symbiont.dist<-rcorr(cooccur.symbiont,type="spearman")
cooccur.symbiont.cor<-cooccur.symbiont.dist$r
cooccur.symbiont.cor.p<-cooccur.symbiont.dist$P
# Multiple testing correction using Benjamini-Hochberg standard false discovery rate correction ("FDR-BH")
cooccur.symbiont.cor.p <- p.adjust(cooccur.symbiont.cor.p, method="BH")
# Consider positive cooccurence at given coefficient (cor.cutoff) and p-value cutoffs
cooccur.symbiont.cor.pos<-cooccur.symbiont.cor
cooccur.symbiont.cor.pos.p<-cooccur.symbiont.cor.p
cooccur.symbiont.cor.pos[which(cooccur.symbiont.cor.pos <= 0.6)]=0
cooccur.symbiont.cor.pos[which(cooccur.symbiont.cor.pos.p>0.01)]=0
# delete those rows and columns with sum = 0
cooccur.symbiont.cor.pos<-cooccur.symbiont.cor.pos[which(rowSums(cooccur.symbiont.cor.pos)!=1),]
cooccur.symbiont.cor.pos<-cooccur.symbiont.cor.pos[,which(colSums(cooccur.symbiont.cor.pos)!=0)]
# Consider both positive and netagive cooccurence at given coefficient (cor.cutoff) and p-value cutoffs
cooccur.symbiont.cor.both<-cooccur.symbiont.cor
cooccur.symbiont.cor.both.p<-cooccur.symbiont.cor.p
cooccur.symbiont.cor.both[which(cooccur.symbiont.cor.both>=(-0.6) & cooccur.symbiont.cor.both <= 0.6)]=0
cooccur.symbiont.cor.both[which(cooccur.symbiont.cor.both.p>0.01)]=0
# delete those rows and columns with sum = 0
cooccur.symbiont.cor.both<-cooccur.symbiont.cor.both[which(rowSums(cooccur.symbiont.cor.both)!=1),]
cooccur.symbiont.cor.both<-cooccur.symbiont.cor.both[,which(colSums(cooccur.symbiont.cor.both)!=0)]
# generate graph using igraph
g.pos.symbiont<-graph.adjacency(cooccur.symbiont.cor.pos,weight=T,mode="undirected")
g.pos.symbiont<-simplify(g.pos.symbiont)
V(g.pos.symbiont)$label <- V(g.pos.symbiont)$name
V(g.pos.symbiont)$degree <- degree(g.pos.symbiont)
g.both.symbiont<-graph.adjacency(cooccur.symbiont.cor.both,weight=T,mode="undirected")
g.both.symbiont<-simplify(g.both.symbiont)
V(g.both.symbiont)$label <- V(g.both.symbiont)$name
V(g.both.symbiont)$degree <- degree(g.both.symbiont)
# append the output into results
result<-list()
result$cooccur.symbiont.cor<-cooccur.symbiont.cor
result$cooccur.symbiont.cor.p<-cooccur.symbiont.cor.p
result$cooccur.symbiont.cor.pos<-cooccur.symbiont.cor.pos
result$graph.pos.symbiont<-g.pos.symbiont
result$cooccur.symbiont.cor.both<-cooccur.symbiont.cor.both
result$graph.both.symbiont<-g.both.symbiont
# plot graph
ig.cols<-c("#FFFF66", "#FF9900", "#FF3333", "#FF3300", "#FF0066", "#FF0033", "#669999", "#660099", "#CCFFFF", "#006699", "#00FF00", "#99FF99", "#999900", "#FFFF99")
V(g.both.symbiont)$color<-ig.cols
plot.igraph(g.both.symbiont)
# save manually
```
```{r Plot host and symbiont ages, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
sort.age<-c("host", symbionts)
age$organism<-reorder.factor(age$organism, new.order=sort.age)
age %>% arrange(organism)
age.plot<-ggplot(data=age, aes(x=as.factor(fct_rev(organism)), y=median, colour=organism)) +
scale_colour_manual(values=symbiont.colors.age) +
geom_crossbar(aes(ymin=min, ymax=max, fill=organism), width = 0.5) +
scale_fill_manual(values=symbiont.colors.age) +
coord_flip() +
theme_minimal() +
#theme(panel.grid.major=element_line(colour="#CCCCCC")) +
#theme(text=element_text(family="Arial")) +
theme(axis.text.x=element_text(angle=-90, hjust=1, size=12)) +
theme(axis.text.y=element_text(size=12)) +
labs(x="", y="Age (Mya)")
age.plot
ggsave(age.plot, file="./plots/ages.eps", height=400, width=400, units="mm")
age.histogram<-ggplot() +
geom_histogram(data=age, aes(x=median), binwidth=50, fill="black") +
theme_minimal() +
theme(axis.text.x=element_text(size=12)) +
theme(axis.text.y=element_text(size=12)) +
labs(x="Age (Mya)", y="Frequency of symbiont divergences")
age.histogram
ggsave(age.histogram, file="./plots/age.histogram.eps", height=400, width=400, units="mm")
age.species.plot<-ggplot(data=age) +
geom_point(aes(x=median, y=sym_species, colour=organism), size=5) +
scale_colour_manual(values=symbiont.colors.age) +
theme_minimal() +
theme(axis.text.x=element_text(size=12)) +
theme(axis.text.y=element_text(size=12)) +
theme(legend.position="none") +
labs(x="Age (Mya)", y="Number of colonized host species") +
geom_rect(aes(xmin=-422.7252, xmax=-97.1542, ymin=0, ymax=55)) +
geom_vline(xintercept=-256.131)
age.species.plot
ggsave(age.species.plot, file="./plots/age.species.plot.eps", height=200, width=200, units="mm")
```
```{bash Generate per symbiont nt distance matrixes, include=FALSE}
for f in $(awk '{print $2}' ./data/abundances.tsv|sort -u|grep -v symbiont); do Rscript --vanilla ./scripts/gen_symbiont_dist_table.R --symbiont $f --output ./data/${f}.nt.dist; done
cat ./data/*nt.dist > ./data/symbiont.nt.dists
sed -i "" "s/\.[A-Z][a-z0-9]*//g" ./data/symbiont.nt.dists
```
```{r Plot pairwise UniFrac/phylogenetic distance of host/symbiont pairs, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
# UniFrac
unifrac.pairwise.dist<-reshape2::melt(as.matrix(unifrac))
unifrac.pairwise.dist$Var<-paste(unifrac.pairwise.dist$Var1,"_",unifrac.pairwise.dist$Var2, sep="")
unifrac.pairwise.dist<-unifrac.pairwise.dist[, c("Var", "value")]
unifrac.pairwise.dist[3]<-"unifrac"
names(unifrac.pairwise.dist)<-c("ID", "sym_dist", "symbiont")
# Symbionts
symbiont.pairwise.dist<-read.csv("./data/symbiont.nt.dists", sep="\t", header=F)
symbiont.pairwise.dist$ID<-paste(symbiont.pairwise.dist$V1, "_", symbiont.pairwise.dist$V2, sep="")
symbiont.pairwise.dist<-symbiont.pairwise.dist[, c("ID", "V3", "V4")]
names(symbiont.pairwise.dist)<-c("ID", "sym_dist", "symbiont")
# Merge UniFrac and symbiont
unifrac.symbiont.pairwise.dist<-rbind(unifrac.pairwise.dist, symbiont.pairwise.dist)
# Host
host.tree.individuals.renamed<-host.tree.individuals
host.tree.individuals.renamed$tip.label<-gsub("(.*)_.*", "\\1", host.tree.individuals.renamed$tip.label)
host.pairwise.dist<-reshape2::melt(cophenetic(host.tree.individuals.renamed))
host.pairwise.dist$Var<-paste(host.pairwise.dist$Var1,"_",host.pairwise.dist$Var2, sep="")
host.pairwise.dist<-host.pairwise.dist[, c("Var", "value")]
names(host.pairwise.dist)<-c("ID", "host_dist")
host.pairwise.all.dist.df<-merge(host.pairwise.dist, unifrac.symbiont.pairwise.dist, by="ID")
# Mitochondria
mito.tree.individuals.renamed<-mito.tree.individuals
mito.tree.individuals.renamed$tip.label<-gsub("(.*)_.*", "\\1", mito.tree.individuals.renamed$tip.label)
mito.pairwise.dist<-reshape2::melt(cophenetic(mito.tree.individuals.renamed))
mito.pairwise.dist$Var<-paste(mito.pairwise.dist$Var1,"_",mito.pairwise.dist$Var2, sep="")
mito.pairwise.dist<-mito.pairwise.dist[, c("Var", "value")]
names(mito.pairwise.dist)<-c("ID", "mito_dist")
mito.pairwise.all.dist.df<-merge(mito.pairwise.dist, unifrac.symbiont.pairwise.dist, by="ID")
# Select most abundant symbiont clades
selection<-c("unifrac","Gamma1", "Alpha10", "Alpha3", "Alpha12", "Spiro1" ,"Delta1", "Delta4", "Delta3", "Gamma3", "Alpha5", "Alpha7")
host.pairwise.dist.unifrac<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='unifrac'),]
host.pairwise.dist.Gamma1<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Gamma1'),]
host.pairwise.dist.Alpha10<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Alpha10'),]
host.pairwise.dist.Alpha3<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Alpha3'),]
host.pairwise.dist.Alpha12<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Alpha12'),]
host.pairwise.dist.Spiro1<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Spiro1'),]
host.pairwise.dist.Delta1<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Delta1'),]
host.pairwise.dist.Delta4<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Delta4'),]
host.pairwise.dist.Delta3<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Delta3'),]
host.pairwise.dist.Gamma3<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Gamma3'),]
host.pairwise.dist.Alpha5<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Alpha5'),]
host.pairwise.dist.Alpha7<-host.pairwise.all.dist.df[which(host.pairwise.all.dist.df$symbiont=='Alpha7'),]
host.pairwise.selection.dist.df<-rbind(host.pairwise.dist.unifrac, host.pairwise.dist.Gamma1, host.pairwise.dist.Alpha10, host.pairwise.dist.Alpha3, host.pairwise.dist.Alpha12, host.pairwise.dist.Spiro1, host.pairwise.dist.Delta1, host.pairwise.dist.Delta4, host.pairwise.dist.Delta3, host.pairwise.dist.Gamma3, host.pairwise.dist.Alpha5, host.pairwise.dist.Alpha7)
host.pairwise.selection.dist.df<-arrange(transform(host.pairwise.selection.dist.df, symbiont=factor(symbiont, levels=selection)), symbiont)
mito.pairwise.dist.unifrac<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='unifrac'),]
mito.pairwise.dist.Gamma1<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Gamma1'),]
mito.pairwise.dist.Alpha10<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Alpha10'),]
mito.pairwise.dist.Alpha3<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Alpha3'),]
mito.pairwise.dist.Alpha12<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Alpha12'),]
mito.pairwise.dist.Spiro1<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Spiro1'),]
mito.pairwise.dist.Delta1<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Delta1'),]
mito.pairwise.dist.Delta4<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Delta4'),]
mito.pairwise.dist.Delta3<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Delta3'),]
mito.pairwise.dist.Gamma3<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Gamma3'),]
mito.pairwise.dist.Alpha5<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Alpha5'),]
mito.pairwise.dist.Alpha7<-mito.pairwise.all.dist.df[which(mito.pairwise.all.dist.df$symbiont=='Alpha7'),]
mito.pairwise.selection.dist.df<-rbind(mito.pairwise.dist.unifrac, mito.pairwise.dist.Gamma1, mito.pairwise.dist.Alpha10, mito.pairwise.dist.Alpha3, mito.pairwise.dist.Alpha12, mito.pairwise.dist.Spiro1, mito.pairwise.dist.Delta1, mito.pairwise.dist.Delta4, mito.pairwise.dist.Delta3, mito.pairwise.dist.Gamma3, mito.pairwise.dist.Alpha5, mito.pairwise.dist.Alpha7)
mito.pairwise.selection.dist.df<-arrange(transform(mito.pairwise.selection.dist.df, symbiont=factor(symbiont, levels=selection)), symbiont)
# Plots
my.lm<-y~x
host.pairwise.dist.selection.plot<-ggplot(data=host.pairwise.selection.dist.df, aes(x=host_dist, y=sym_dist)) +
geom_point(aes(color=symbiont), size=1) +
geom_density_2d(color="black", size=0.1) +
geom_smooth(method='lm', formula=my.lm, color="black", linetype="dotted", size=0.5) +
stat_poly_eq(formula=my.lm,
aes(label=..rr.label..),
parse=TRUE) +
facet_wrap(~ symbiont, scales="free_y") +
scale_colour_manual(values=c(symbiont.colors, "unifrac"="gainsboro")) +
theme_minimal() +
theme(text=element_text(size=5)) +
theme(legend.position="none")