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figures.R
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setwd("")
library(data.table)
library(outliers)
#hash second line work with full data, unhash to work with data minus the outlier
rDNA_by_taxa<- read.csv("genome_size.csv", sep = ",", header = TRUE)
rDNA_by_taxa<- rDNA_by_taxa[ rDNA_by_taxa$Genus!="Basidiobolus",]
#mean CN
mean(rDNA_by_taxa$CN)
median(rDNA_by_taxa$CN)
#min and max
range(rDNA_by_taxa$CN)
#n by phyla
sum(rDNA_by_taxa$group == "Ascomycota")
sum(rDNA_by_taxa$group == "Basidiomycota")
sum(rDNA_by_taxa$group == "Lower")
#suillus only
suillus<- rDNA_by_taxa[rDNA_by_taxa$Genus == "Suillus",]
#no brev.
suillus.wo.brev<- suillus[suillus$SE != "brevipes",]
range(suillus.wo.brev$CN)
#figure 1.d (Phylum)
boxplot(rDNA_by_taxa$CN ~rDNA_by_taxa$group, main = "no_outlier")
cochran.test(CN~group, rDNA_by_taxa, inlying=FALSE)
aov<- aov(rDNA_by_taxa$CN~rDNA_by_taxa$group, rDNA_by_taxa)
summary(aov(rDNA_by_taxa$CN~rDNA_by_taxa$group, rDNA_by_taxa))
hsd<- TukeyHSD(aov)
hsd
###Fig2
#figure 2.a by trophic mode
#order goups for plotting
rDNA_by_taxa$TrophicMode=factor(rDNA_by_taxa$TrophicMode,c("Pathotroph","Saprotroph","Symbiotroph","Multi"))
#get n
b <- boxplot(CN ~TrophicMode, data=rDNA_by_taxa, plot=0)
#set pars
par(mar=c(7,5,1,1))
par(cex.axis=0.7)
boxplot(CN ~TrophicMode, data=rDNA_by_taxa,
range = 3,
outline=TRUE,
names=paste(b$names, "(n=", b$n, ")"),
las = 2,
main = "Trophic mode",
col = "#5D2D8B",
ylim = c(0,350))
#Fig. 2.b Guild for Ascos, without outlier
#make subset dataframe of just ascos
#see how many catagories have 5 or more
Asco_df<- rDNA_by_taxa[rDNA_by_taxa$Phylum == "Ascomycota",]
b <- boxplot(CN ~ Lifestyle, data=Asco_df, plot=0)
par(mar=c(7,5,1,1))
par(cex.axis=0.7)
boxplot(CN ~Lifestyle, data=Asco_df,
range = 3,
outline=TRUE,
names=paste(b$names, "(n=", b$n, ")"),
las = 2,
main = "CN by trophic mode - no outlier")
#only Pathogen, SAP S/L/O and PAthogen / SAP S/L/O do
#make new DF
#Asco_df_with_enough_reps<- Asco_df[Asco_df$Lifestyle == "SAP S/L/O" | Asco_df$Lifestyle == "Pathogen" | Asco_df$Lifestyle == "SAP S/L/O / pathogen",]
Asco_df_with_enough_reps<- droplevels(subset(Asco_df[(Asco_df$Lifestyle == "SAP S/L/O") | (Asco_df$Lifestyle == "Pathogen") | (Asco_df$Lifestyle == "SAP S/L/O / pathogen"),]))
c <- boxplot(Asco_df_with_enough_reps$CN ~ Asco_df_with_enough_reps$Lifestyle, data=Asco_df_with_enough_reps, plot=0)
par(mar=c(7,5,1,1))
par(cex.axis=0.7)
boxplot(Asco_df_with_enough_reps$CN ~ Asco_df_with_enough_reps$Lifestyle, data=Asco_df_with_enough_reps,
range = 3,
outline=TRUE,
names=paste(c$names, "(n=", c$n, ")"),
las = 2,
main = "Ascomycota - no outlier")
cochran.test(CN ~ Lifestyle, data=Asco_df_with_enough_reps, inlying=FALSE)
#stripchart of above with no sap SLO/pathogen
how_many<- boxplot(Asco_df_with_enough_reps$CN ~ Asco_df_with_enough_reps$Lifestyle, data=Asco_df_with_enough_reps, plot=0)
Asco_df_with_enough_reps<- droplevels(subset(Asco_df[(Asco_df$Lifestyle == "SAP S/L/O") | (Asco_df$Lifestyle == "Pathogen"),]))
stripchart(Asco_df_with_enough_reps$CN ~ Asco_df_with_enough_reps$Lifestyle,
main = "Guild - Ascomycota",
method="jitter",
pch = 16,
cex = 1.8,
vertical=TRUE,
col = "#006666",
at=c(1.1,1.9),
ylim = c(0, 350))
how_many$n
#Fig. 2.c Guild for Basids, without outlier
#see how many catagories have 5 or more BASIDS
Basid_df<- rDNA_by_taxa[rDNA_by_taxa$Phylum == "Basidiomycota",]
b <- boxplot(CN ~ Lifestyle, data=Basid_df, plot=0)
par(mar=c(7,5,1,1))
par(cex.axis=0.7)
boxplot(CN ~Lifestyle, data=Basid_df,
range = 3,
outline=TRUE,
names=paste(b$names, "(n=", b$n, ")"),
las = 2,
main = "CN by trophic mode - no outlier")
#only ECM, SAP BR and SAP WR
#make new DF
Basid_df_with_enough_reps<- droplevels(subset(Basid_df[(Basid_df$Lifestyle == "ECM") | (Basid_df$Lifestyle == "SAP BR") | (Basid_df$Lifestyle == "SAP WR"),]))
#strip chart
Basid_df_with_enough_reps$Lifestyle <- as.character(Basid_df_with_enough_reps$Lifestyle)
Basid_df_with_enough_reps.2<- replace(Basid_df_with_enough_reps$Lifestyle, Basid_df_with_enough_reps$Lifestyle=="SAP BR", "Wood Rot")
Basid_df_with_enough_reps.3<- replace(Basid_df_with_enough_reps.2, Basid_df_with_enough_reps.2=="SAP WR", "Wood Rot")
Basid_df_with_enough_reps.4<- cbind(Basid_df_with_enough_reps, Basid_df_with_enough_reps.3)
stripchart(Basid_df_with_enough_reps.4$CN ~ Basid_df_with_enough_reps.4$Basid_df_with_enough_reps.3,
main = "Guild - Basidiomycota",
method="jitter",
pch = 16,
cex = 1.8,
vertical=TRUE,
col = "#CCCB64",
at=c(1.1,1.9),
ylim = c(0,350))
#get n
sum(Basid_df_with_enough_reps.4$Basid_df_with_enough_reps.3 == "ECM")
sum(Basid_df_with_enough_reps.4$Basid_df_with_enough_reps.3 == "Wood Rot")
#run stats
#2.a by Trophic mode
cochran.test(CN ~ TrophicMode, data=rDNA_by_taxa, inlying=FALSE)
#cochrans is significant. have to run KW test rather than anova.
#summary(aov(CN ~TrophicMode, data=rDNA_by_taxa))
kruskal.test(CN ~TrophicMode, data=rDNA_by_taxa) # kw not significant.
#2.b by Trophic mode ASCOS
cochran.test(CN ~ Lifestyle, data=Asco_df_with_enough_reps, inlying=FALSE)
#not sig, run anova
summary(aov(CN ~ Lifestyle, data=Asco_df_with_enough_reps))
#not sig dif.
#2.c by Trophic mode BASIDS
cochran.test(CN ~ Lifestyle, data=Basid_df_with_enough_reps, inlying=FALSE)
#not sig, run anova
summary(aov(CN ~ Lifestyle, data=Basid_df_with_enough_reps))
#not sig dif.
###For fig S1
setwd()
library(data.table)
library(outliers)
rDNA_by_taxa<- read.csv("genome_size.csv", sep = ",", header = TRUE)
rDNA_by_taxa<- rDNA_by_taxa[ rDNA_by_taxa$Genus!="Basidiobolus",] #remove outlier
#at the species level (collapse species with more than one representation into a random representation of that species)
get_HSD_species <- function() {
split.df.by.SE<- split(rDNA_by_taxa, rDNA_by_taxa$SE)
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
single_species_representation.df <-lapply(names(split.df.by.SE), function(x) randomRows(df = split.df.by.SE[[x]], n = 1))
single_species_representation.df.df <- data.frame(matrix(unlist(single_species_representation.df), nrow=length(single_species_representation.df), byrow=T))
aov<- aov(single_species_representation.df.df$X12~single_species_representation.df.df$X13, single_species_representation.df.df)
summary(aov)
hsd<- TukeyHSD(aov)
TK<-(hsd)
TK_data<-as.data.frame(TK[1:1])
results<-data.frame(TK_data[1:3, 4], row.names = row.names(TK_data))
return(results)
}
#run 1000 times
AOV_results_df_SE<- as.data.frame(replicate(10000, get_HSD_species(), simplify = "array"))
#get results of the table, numeric
Basidiomycota_Ascomycota_SE<- sum(AOV_results_df_SE[1,1:ncol(AOV_results_df_SE)] < 0.05)
Lower_Ascomycota_SE<- sum(AOV_results_df_SE[2,1:ncol(AOV_results_df_SE)] < 0.05)
Lower_Basidiomycota_SE <- sum(AOV_results_df_SE[3,1:ncol(AOV_results_df_SE)] < 0.05)
Basidiomycota_Ascomycota_SE
Lower_Ascomycota_SE
Lower_Basidiomycota_SE
#at the genus level
get_HSD_genus <- function() {
split.df.by.genus<- split(rDNA_by_taxa, rDNA_by_taxa$Genus)
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
single_genus_representation.df <-lapply(names(split.df.by.genus), function(x) randomRows(df = split.df.by.genus[[x]], n = 1))
single_genus_representation.df.df <- data.frame(matrix(unlist(single_genus_representation.df), nrow=length(single_genus_representation.df), byrow=T))
aov<- aov(single_genus_representation.df.df$X12~single_genus_representation.df.df$X13, single_genus_representation.df.df)
summary(aov)
hsd<- TukeyHSD(aov)
TK<-(hsd)
TK_data<-as.data.frame(TK[1:1])
results<-data.frame(TK_data[1:3, 4], row.names = row.names(TK_data))
return(results)
}
AOV_results_df_genus<- as.data.frame(replicate(10000, get_HSD_genus(), simplify = "array"))
#get results of the table, numeric
Basidiomycota_Ascomycota_genus<- sum(AOV_results_df_genus[1,1:ncol(AOV_results_df_genus)] < 0.05)
Lower_Ascomycota_genus<- sum(AOV_results_df_genus[2,1:ncol(AOV_results_df_genus)] < 0.05)
Lower_Basidiomycota_genus <- sum(AOV_results_df_genus[3,1:ncol(AOV_results_df_genus)] < 0.05)
Basidiomycota_Ascomycota_genus
Lower_Ascomycota_genus
Lower_Basidiomycota_genus
#at the family level
get_HSD_family <- function() {
split.df.by.family<- split(rDNA_by_taxa, rDNA_by_taxa$Family)
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
single_family_representation.df <-lapply(names(split.df.by.family), function(x) randomRows(df = split.df.by.family[[x]], n = 1))
single_family_representation.df.df <- data.frame(matrix(unlist(single_family_representation.df), nrow=length(single_family_representation.df), byrow=T))
aov<- aov(single_family_representation.df.df$X12~single_family_representation.df.df$X13, single_family_representation.df.df)
summary(aov)
hsd<- TukeyHSD(aov)
TK<-(hsd)
TK_data<-as.data.frame(TK[1:1])
results<-data.frame(TK_data[1:3, 4], row.names = row.names(TK_data))
return(results)
}
AOV_results_df_family<- as.data.frame(replicate(10000, get_HSD_family(), simplify = "array"))
#get results of the table, numeric
Basidiomycota_Ascomycota_family<- sum(AOV_results_df_family[1,1:ncol(AOV_results_df_family)] < 0.05)
Lower_Ascomycota_family<- sum(AOV_results_df_family[2,1:ncol(AOV_results_df_family)] < 0.05)
Lower_Basidiomycota_family <- sum(AOV_results_df_family[3,1:ncol(AOV_results_df_family)] < 0.05)
Basidiomycota_Ascomycota_family
Lower_Ascomycota_family
Lower_Basidiomycota_family
#at the order level
get_HSD_order <- function() {
split.df.by.order<- split(rDNA_by_taxa, rDNA_by_taxa$Order)
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
single_order_representation.df <-lapply(names(split.df.by.order), function(x) randomRows(df = split.df.by.order[[x]], n = 1))
single_order_representation.df.df <- data.frame(matrix(unlist(single_order_representation.df), nrow=length(single_order_representation.df), byrow=T))
aov<- aov(single_order_representation.df.df$X12~single_order_representation.df.df$X13, single_order_representation.df.df)
summary(aov)
hsd<- TukeyHSD(aov)
TK<-(hsd)
TK_data<-as.data.frame(TK[1:1])
results<-data.frame(TK_data[1:3, 4], row.names = row.names(TK_data))
return(results)
}
AOV_results_df_order<- as.data.frame(replicate(10000, get_HSD_order(), simplify = "array"))
#get results of the table, numeric
Basidiomycota_Ascomycota_order<- sum(AOV_results_df_order[1,1:ncol(AOV_results_df_order)] < 0.05)
Lower_Ascomycota_order<- sum(AOV_results_df_order[2,1:ncol(AOV_results_df_order)] < 0.05)
Lower_Basidiomycota_order <- sum(AOV_results_df_order[3,1:ncol(AOV_results_df_order)] < 0.05)
Basidiomycota_Ascomycota_order
Lower_Ascomycota_order
Lower_Basidiomycota_order
#at the class level
get_HSD_class <- function() {
split.df.by.class<- split(rDNA_by_taxa, rDNA_by_taxa$Class)
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
single_class_representation.df <-lapply(names(split.df.by.class), function(x) randomRows(df = split.df.by.class[[x]], n = 1))
single_class_representation.df.df <- data.frame(matrix(unlist(single_class_representation.df), nrow=length(single_class_representation.df), byrow=T))
aov<- aov(single_class_representation.df.df$X12~single_class_representation.df.df$X13, single_class_representation.df.df)
summary(aov)
hsd<- TukeyHSD(aov)
TK<-(hsd)
TK_data<-as.data.frame(TK[1:1])
results<-data.frame(TK_data[1:3, 4], row.names = row.names(TK_data))
return(results)
}
AOV_results_df_class<- as.data.frame(replicate(10000, get_HSD_class(), simplify = "array"))
#get results of the table, numeric
Basidiomycota_Ascomycota_class<- sum(AOV_results_df_class[1,1:ncol(AOV_results_df_class)] < 0.05)
Lower_Ascomycota_class<- sum(AOV_results_df_class[2,1:ncol(AOV_results_df_class)] < 0.05)
Lower_Basidiomycota_class <- sum(AOV_results_df_class[3,1:ncol(AOV_results_df_class)] < 0.05)
Basidiomycota_Ascomycota_class
Lower_Ascomycota_class
Lower_Basidiomycota_class
#mosaic plots
par(mfrow=c(1,5))
#species
p.vad.dist.df.species<- rbind("A-B" = c(Basidiomycota_Ascomycota_SE, 10000-Basidiomycota_Ascomycota_SE))
p.vad.dist.df.species<- rbind(p.vad.dist.df.species, "A-L" = c(Lower_Ascomycota_SE, 10000-Lower_Ascomycota_SE))
p.vad.dist.df.species<- rbind(p.vad.dist.df.species,"L-B" = c(Lower_Basidiomycota_SE, 10000- Lower_Basidiomycota_SE))
rownames(p.vad.dist.df.species)<- c("A-B", "A-L", "L-B")
mosaicplot(p.vad.dist.df.species, ylab = "relative frequency", main = "Species", col=c("black", "dark grey"), border = "white", margin = NULL, cex.axis = 0.66, off= 0)
#genus
p.vad.dist.df.genus<- rbind("A-B" = c(Basidiomycota_Ascomycota_genus, 10000-Basidiomycota_Ascomycota_genus))
p.vad.dist.df.genus<- rbind(p.vad.dist.df.genus, "A-L" = c(Lower_Ascomycota_genus, 10000-Lower_Ascomycota_genus))
p.vad.dist.df.genus<- rbind(p.vad.dist.df.genus,"L-B" = c(Lower_Basidiomycota_genus, 10000- Lower_Basidiomycota_genus))
rownames(p.vad.dist.df.genus)<- c("A-B", "A-L", "L-B")
mosaicplot(p.vad.dist.df.genus, ylab = "relative frequency", main = "Genus", col=c("black", "dark grey"), border = "white", margin = NULL, cex.axis = 0.66, off= 0)
#family
p.vad.dist.df.family<- rbind("A-B" = c(Basidiomycota_Ascomycota_family, 10000-Basidiomycota_Ascomycota_family))
p.vad.dist.df.family<- rbind(p.vad.dist.df.family, "A-L" = c(Lower_Ascomycota_family, 10000-Lower_Ascomycota_family))
p.vad.dist.df.family<- rbind(p.vad.dist.df.family,"L-B" = c(Lower_Basidiomycota_family, 10000- Lower_Basidiomycota_family))
rownames(p.vad.dist.df.family)<- c("A-B", "A-L", "L-B")
mosaicplot(p.vad.dist.df.family, ylab = "relative frequency", main = "Family", col=c("black", "dark grey"), border = "white", margin = NULL, cex.axis = 0.66, off= 0)
#order
p.vad.dist.df.order<- rbind("A-B" = c(Basidiomycota_Ascomycota_order, 10000-Basidiomycota_Ascomycota_order))
p.vad.dist.df.order<- rbind(p.vad.dist.df.order, "A-L" = c(Lower_Ascomycota_order, 10000-Lower_Ascomycota_order))
p.vad.dist.df.order<- rbind(p.vad.dist.df.order,"L-B" = c(Lower_Basidiomycota_order, 10000- Lower_Basidiomycota_order))
rownames(p.vad.dist.df.order)<- c("A-B", "A-L", "L-B")
mosaicplot(p.vad.dist.df.order, ylab = "relative frequency", main = "Order", col=c("black", "dark grey"), border = "white", margin = NULL, cex.axis = 0.66, off= 0)
#class
p.vad.dist.df.class<- rbind("A-B" = c(Basidiomycota_Ascomycota_class, 10000-Basidiomycota_Ascomycota_class))
p.vad.dist.df.class<- rbind(p.vad.dist.df.class, "A-L" = c(Lower_Ascomycota_class, 10000-Lower_Ascomycota_class))
p.vad.dist.df.class<- rbind(p.vad.dist.df.class,"L-B" = c(Lower_Basidiomycota_class, 10000- Lower_Basidiomycota_class))
rownames(p.vad.dist.df.class)<- c("A-B", "A-L", "L-B")
mosaicplot(p.vad.dist.df.class, main = "Class", col=c("black", "dark grey"), border = "white", margin = NULL, cex.axis = 0.66, off= 0)
#get n for each category
#species
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
split.df.by.SE<- split(rDNA_by_taxa, rDNA_by_taxa$SE)
single_species_representation.df <-lapply(names(split.df.by.SE), function(x) randomRows(df = split.df.by.SE[[x]], n = 1))
single_species_representation.df.df <- data.frame(matrix(unlist(single_species_representation.df), nrow=length(single_species_representation.df), byrow=T))
n.speies<- nrow(single_species_representation.df.df)
n.speies
n.asco<-sum(single_species_representation.df.df$X13 == "Ascomycota")
n.asco
n.basid<- sum(single_species_representation.df.df$X13 == "Basidiomycota")
n.basid
n.lower<- sum(single_species_representation.df.df$X13 == "Lower")
n.lower
split.df.by.genus<- split(rDNA_by_taxa, rDNA_by_taxa$Genus)
single_genus_representation.df <-lapply(names(split.df.by.genus), function(x) randomRows(df = split.df.by.genus[[x]], n = 1))
single_genus_representation.df.df <- data.frame(matrix(unlist(single_genus_representation.df), nrow=length(single_genus_representation.df), byrow=T))
n.genus<- nrow(single_genus_representation.df.df)
n.genus
n.asco<-sum(single_genus_representation.df.df$X13 == "Ascomycota")
n.asco
n.basid<- sum(single_genus_representation.df.df$X13 == "Basidiomycota")
n.basid
n.lower<- sum(single_genus_representation.df.df$X13 == "Lower")
n.lower
split.df.by.family<- split(rDNA_by_taxa, rDNA_by_taxa$Family)
single_family_representation.df <-lapply(names(split.df.by.family), function(x) randomRows(df = split.df.by.family[[x]], n = 1))
single_family_representation.df.df <- data.frame(matrix(unlist(single_family_representation.df), nrow=length(single_family_representation.df), byrow=T))
n.family<- nrow(single_family_representation.df.df)
n.family
n.asco<-sum(single_family_representation.df.df$X13 == "Ascomycota")
n.asco
n.basid<- sum(single_family_representation.df.df$X13 == "Basidiomycota")
n.basid
n.lower<- sum(single_family_representation.df.df$X13 == "Lower")
n.lower
split.df.by.order<- split(rDNA_by_taxa, rDNA_by_taxa$Order)
single_order_representation.df <-lapply(names(split.df.by.order), function(x) randomRows(df = split.df.by.order[[x]], n = 1))
single_order_representation.df.df <- data.frame(matrix(unlist(single_order_representation.df), nrow=length(single_order_representation.df), byrow=T))
n.order<- nrow(single_order_representation.df.df)
n.order
n.asco<-sum(single_order_representation.df.df$X13 == "Ascomycota")
n.asco
n.basid<- sum(single_order_representation.df.df$X13 == "Basidiomycota")
n.basid
n.lower<- sum(single_order_representation.df.df$X13 == "Lower")
n.lower
split.df.by.class<- split(rDNA_by_taxa, rDNA_by_taxa$Class)
single_class_representation.df <-lapply(names(split.df.by.class), function(x) randomRows(df = split.df.by.class[[x]], n = 1))
single_class_representation.df.df <- data.frame(matrix(unlist(single_class_representation.df), nrow=length(single_class_representation.df), byrow=T))
n.class<- nrow(single_class_representation.df.df)
n.class
n.asco<-sum(single_class_representation.df.df$X13 == "Ascomycota")
n.asco
n.basid<- sum(single_class_representation.df.df$X13 == "Basidiomycota")
n.basid
n.lower<- sum(single_class_representation.df.df$X13 == "Lower")
n.lower