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Bautista2024_Supplementary_NatureCommunications.R
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##Supplementary Figures Bautista_2024###
#In order to place the figures in the same folder as the data tables,
#you need to specify the name of this directory in the 'Set Directory' section below.
####Packages####
rm(list=ls())
#install.packages("agricolae")
library(agricolae)
#if (!require("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
library(BiocManager)
#install.packages("broom")
library(broom)
#install.packages("car",type="binary")
library(car)
#install.packages("cowplot")
library(cowplot)
#install.packages("data.table")
library(data.table)
#install.packages("dplyr")
library(dplyr)
#install.packages("drc")
library(drc)
#install.packages("egg")
library(egg)
#install.packages("factoextra")
library(factoextra)
#install.packages("foreign")
library(foreign)
#install.packages("flowCore")
library(flowCore)
#install.packages("flowViz")
library(flowViz)
#install.packages("gdata")
library(gdata)
#install.packages("ggimage")
library(ggimage)
#install.packages("ggpattern")
library(ggpattern)
#install.packages("ggsignif")
library(ggsignif)
#install.packages("ggtext")
library(ggtext)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("ggprism")
library(ggprism)
#install.packages("grid")
library(ggpubr)
#install.packages("ggthemes")
library(ggthemes)
#BiocManager::install("ggtree")
library(ggtree)
#install.packages("glue")
library(glue)
#install.packages("grid")
library(grid)
#install.packages("gridExtra")
library(gridExtra)
#install.packages("growthcurver")
library(growthcurver)
#install.packages("gtable")
library(gtable)
#install.packages("janitor")
library(janitor)
#install.packages("lme4",type="binary")
library(lme4)
#install.packages("magick")
library(magick)
#install.packages("magrittr")
library(magrittr)
#install.packages("Matrix")
library(Matrix)
#install.packages("modelr")
library(modelr)
#install.packages("multcomp")
library(multcomp)
#install.packages("multcompView")
library(multcompView)
#install.packages("nlme")
library(nlme)
#install.packages("pdftools")
library(pdftools)
#install.packages("pegas")
library(pegas)
#install.packages("plyr")
library(plyr)
#install.packages("png")
library(png)
#install.packages("quantreg", type="binary")
library(quantreg)
#install.packages("readr")
library(readr)
#install.packages("readxl")
library(readxl)
#install.packages("reshape2")
library(reshape2)
#install.packages("rstatix")
library(rstatix)
#install.packages("stats")
library(stats)
#install.packages("stringr")
library(stringr)
#install.packages("svglite")
library(svglite)
#BiocManager::install("trackViewer")
library(trackViewer)
#install.packages("tidyr")
library(tidyr)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("UpSetR")
library(UpSetR)
#BiocManager::install("VariantAnnotation")
library(VariantAnnotation)
#install.packages("vcfR")
library(vcfR)
#################
####Set directory####
#Define the directory where you save all the data from Bautista_2024
setwd("")
#################
####Get Data####
Supp_coverage <- read_csv("1Supp_coverage.csv")
Supp_genomics <- read_csv("2Supp_growth.csv")
Supp_ploidy <- read.csv("4Supp_ploidy.csv",stringsAsFactors = F)
Supp_ploidy_heatmap <- read.csv("4Supp_ploidy_heatmap.csv")
Supp_Table_counts_aneuploidies <- read.csv("5Supp_Table_counts_aneuploidies.csv")
Supp_Aneuploidy_chrom <- read_excel("6Supp_Aneuploidy_chrom.xlsx")
Supp_genome <- read_csv("7Supp_new_genome3c_new.csv")
Supp_LOH_chrom<-read_csv("8Supp_Table_LOH.csv")
Supp_counts_LOH <- read_csv("8Supp_Table_counts_LOH.csv")
Supp_SNPs<- read_csv("9Supp_SNP.csv")
Supp_GO<-read_csv("9Supp_GO_all_changes.csv")
Supp10_GO <- image_read_pdf("10Supp_GO.pdf")
Supp_PDR1 <- readPNG("11Supp_PDR1.png")
Supp_candida <-image_read_pdf("12Supp_candida.pdf")
Supp_raw_cov<-read.csv("13Supp_raw_cov.csv")
Supp_growth<-read_csv("14Supp_growth.csv")
Supp_expression<-read_csv("14Supp_expression.csv")
Supp_growth_plasmids<-read.csv("15Supp_growth_plasmids.csv")
Supp_growth_curves_plasmids<-read.csv("15Supp_growth_curves_plasmids.csv")
Supp_growth_plasmids2<-read.csv("15Supp_growth_plasmids2.csv")
Supp_growth_curves_plasmids2<-read.csv("15Supp_growth_curves_plasmids2.csv")
Supp16_boxplot <- read_csv("16Supp_boxplot.csv")
Supp16_curves <- read_csv("16Supp_curves.csv")
Supp_expevol<-read_csv("17Supp_expevol.csv")
Supp17_Sanger <-image_read_pdf("17Supp_Sanger.pdf")
Supp17_LOH_table_parents <- read_excel("17Supp_LOH_parents_table.xlsx")
Supp17_LOH_parents_table_proportion<-read_excel("17Supp_LOH_parents_table_proportion.xlsx")
Supp17_LOH_parents_table_proportion_hybrid<-read_csv("17Supp_LOH_parents_table_proportion_hybrid.csv")
Supp17_LOH_line13_final<-read_excel("17Supp_LOH_line13_final.xlsx")
Supp17_LOH_line28_final<- read_excel("17Supp_LOH_line28_final.xlsx")
Supp17_LOH_line30_final<-read_excel("17Supp_LOH_line30_final.xlsx")
Supp_loh_table_day2<-read_csv("18Supp_loh_table_day2.csv")
Supp_fdata<-read_csv("18Supp_fdata.csv")
##########
############Figure Supplementary 1############
#Define some functions
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
Supp_coverage$library <- "Sample"
hybrid <-Supp_coverage %>% filter(Strain=="3Hybrid")
hybrid_noNQO <- hybrid %>% filter(Type!="Evolved_NQO")
hybrid_NQO <- hybrid %>% filter(Type=="Evolved_NQO")
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=10)
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=25)
nohybrid <-Supp_coverage %>% filter(Strain!="3Hybrid")
joined <- rbind(nohybrid,hybrid_noNQO,hybrid_NQO)
joined$Replicate<-as.numeric(joined$Replicate)
Supp_coverage<-joined
mean(Supp_coverage$Mean_Read_Depth,na.rm = TRUE)
FigA <- Supp_coverage %>%
ggplot(aes(y=Mean_Read_Depth, x=library))+
theme_prism()+
geom_boxplot(outlier.size=0.1,col="black",alpha=0.2)+
stat_summary(fun=mean, geom="point", shape=20, size=3, col="red4",fill="red4") +
annotate("text",
y = 125,
x = 1,
label = "105.5",
family = "", fontface = 1, size=3,
col="red4")+
ylab("Mean read depth")+
scale_x_discrete(breaks=c("Sample"),labels=c("All the \n samples")) +
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=11, face = "bold"),
strip.text = element_text(face = "bold", size = 9),
strip.background = element_blank())+
theme(legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
axis.text= element_text(size=9))
Supp_coverage$Type <- as.factor(Supp_coverage$Type)
Supp_coverage$Strain <- as.factor(Supp_coverage$Strain)
amod <- aov(Mean_Read_Depth~Type*Strain, data=Supp_coverage)
summary(amod)
legend_title <- " "
FigA1 <- Supp_coverage %>%
ggplot(aes(x=Type,y=Mean_Read_Depth, col=Strain, group=interaction(Strain,Type)))+
geom_boxplot(outlier.size=0.1,col="black",aes(fill=Strain),alpha=0.2)+
theme_prism() +
theme_bw(base_size=24) +
scale_fill_manual(legend_title,values=c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme(legend.text.align = 0) +
ylab("Mean read depth")+
theme(legend.position="bottom",
axis.title = element_text(size=14, face = "bold"),
strip.text = element_text(face = "bold", size = 9),
panel.grid.major = element_blank(),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=10),
panel.background = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 25, hjust=1,size=10)) +
scale_x_discrete(limit =c("Ancestor","Evolved_control", "Evolved_NQO"),
label= c("Ancestor","Evolved in control","Evolved in UV mimetic"))+
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=12),
legend.text = element_text(size=10))
leg1 <- get_legend(FigA1)
FigB <- Supp_coverage %>%
ggplot(aes(x=Type,y=Mean_Read_Depth, col=Strain, group=interaction(Strain,Type)))+
geom_boxplot(outlier.size=0.1,col="black",aes(fill=Strain),alpha=0.2)+
scale_fill_manual(legend_title,values=c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_prism() +
theme(axis.text.x = element_text()) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))+
ylab("Mean read depth")+
theme(panel.grid.major = element_blank(),
axis.text.y = element_text(size=13),
panel.background = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 25, hjust=1,size=13)) +
scale_x_discrete(limit =c("Ancestor","Evolved_control", "Evolved_NQO"),
label= c("Ancestor","Evolved\n in control","Evolved in \n UV mimetic"))+
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=12),
legend.text = element_text(size=10))+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=11, face = "bold"),
strip.text = element_text(face = "bold", size = 9),
strip.background = element_blank())+
theme(legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
axis.text= element_text(size=9))
#################
############Assemble and save Supplementary Figure 1############
FigA_label <- plot_grid(FigA, labels = c("a"),label_size=20)
FigA2_label <- plot_grid(FigB, labels = c("b"),label_size=20)
Fig1 <- plot_grid(FigA_label,FigA2_label,nrow = 1)
Fig1leg <- plot_grid(leg1,Fig1, nrow = 2, rel_heights = c(2,12))
ggsave (plot = Fig1leg, filename = "Supplementary_Fig1_low_quality.jpg", units = "cm", device = "jpg",width = 20, height =12, dpi = 300,bg = "white")
ggsave (plot = Fig1leg, filename = "Supplementary_Fig1.png", units = "cm", device = "png",width = 20, height =12, dpi = 1000,bg = "white")
ggsave (plot = Fig1leg, filename = "Supplementary_Fig1.jpg", units = "cm", device = "jpg",width = 20, height =12, dpi = 1000,bg = "white")
ggsave (plot = Fig1leg, filename = "Supplementary_Fig1.svg", units = "cm", device = "svg",width = 20, height =12, dpi = 1000,bg = "white")
ggsave (plot = Fig1leg, filename = "Supplementary_Fig1.pdf", units = "cm", device = "pdf",width = 20, height =12, dpi = 1000,bg = "white")
#################
############Figure Supplementary 2############
#NQO
AV<- dplyr::filter(Supp_genomics, condition=="NQO")
nq <- dplyr::filter(AV, Evolved=="Evolved_NQO")
con <- dplyr::filter(AV, Evolved=="Evolved_control")
anc <- dplyr::filter(AV, Evolved=="Ancestor")
AVB<-rbind(nq,con,anc)
#Define some functions
my_comparisons <- list( c("1.1Scer", "2.1Scer"), c("1.3Hybrid","2.3Hybrid"),
c("1.2Spar", "2.2Spar"))
cond.labs <- c(
`Evolved_control` = "Evolved in control",
`Evolved_NQO` = "Evolved in UV mimetic",
`Ancestor` = "Ancestor"
)
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
my_comparisons <- list( c("1Scer", "2Spar"), c("1Scer","3Hybrid"),
c("2Spar", "3Hybrid"))
legend_title <- " "
hybrid <-AVB %>% filter(Specie=="3Hybrid")
hybrid_noNQO <- hybrid %>% filter(Evolved!="Evolved_NQO")
hybrid_NQO <- hybrid %>% filter(Evolved=="Evolved_NQO")
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=10)
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=25)
nohybrid <-AVB %>% filter(Specie!="3Hybrid")
AVB <- rbind(nohybrid,hybrid_noNQO,hybrid_NQO)
Fig_ext1 <- AVB %>% dplyr::filter(day==2)%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in UV mimetic conditions") +
scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(legend_title,values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
theme_prism() +
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.position="top",legend.text.align = 0) +
guides(color = guide_legend(title = "Genotype")) +
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +ylim(0,0.8)+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=25,angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=0.1,
legend.text = element_text(size=15),
legend.direction="horizontal")+ylim(0,1)
legfitness <- get_legend(Fig_ext1)
daytwo <- AVB %>% dplyr::filter(day==2)
AVB1<-AVB %>%dplyr::filter(Evolved=="Evolved_NQO")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
Fig_ext1_A <- daytwo %>% dplyr::filter(Evolved=="Evolved_NQO")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in UV mimetic conditions") +
scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.67, yend=0.67)) +
geom_segment(aes(x=2, xend=3, y=0.70, yend=0.70)) +
geom_segment(aes(x=1, xend=3, y=0.74, yend=0.74)) +
annotate("text", x = 1, y = 0.78, size = 3,
label = c("p < 0.0001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.69, 0.72, 0.76),
x = c(1.5, 2.5, 2),
label = c("p < 0.05", "p < 0.001", "p < 0.001"),
family = "", fontface = 3, size=3)+
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +ylim(0,0.8)+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+ylim(0,1)
AVB1<-AVB %>%dplyr::filter(Evolved=="Evolved_control")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
Fig_ext1_B <- daytwo %>% dplyr::filter(Evolved=="Evolved_control")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in UV mimetic conditions") + scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.67, yend=0.67)) +
geom_segment(aes(x=2, xend=3, y=0.70, yend=0.70)) +
geom_segment(aes(x=1, xend=3, y=0.74, yend=0.74)) +
annotate("text", x = 1, y = 0.78, size = 3,
label = c("p < 0.0001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.69, 0.72, 0.76),
x = c(1.5, 2.5, 2),
label = c("p < 0.0001", "p > 0.05", "p < 0.05"),
family = "", fontface = 3, size=3) +
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +ylim(0,0.8)+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+ylim(0,1)
AVB1<-AVB %>%dplyr::filter(Evolved=="Ancestor")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
Fig_ext1_C <- daytwo %>% dplyr::filter(Evolved=="Ancestor")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in UV mimetic conditions") + scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.67, yend=0.67)) +
geom_segment(aes(x=2, xend=3, y=0.70, yend=0.70)) +
geom_segment(aes(x=1, xend=3, y=0.74, yend=0.74)) +
annotate("text", x = 1, y = 0.78, size = 3,
label = c("p < 0.0001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.69, 0.72, 0.76),
x = c(1.5, 2.5, 2),
label = c("p < 0.0001", "p < 0.0001", "p < 0.001"),
family = "", fontface = 3, size=3) +
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +ylim(0,0.8)+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+ylim(0,1)
#CONTROL
AV<- dplyr::filter(Supp_genomics, condition=="Control")
nq <- dplyr::filter(AV, Evolved=="Evolved_NQO")
con <- dplyr::filter(AV, Evolved=="Evolved_control")
anc <- dplyr::filter(AV, Evolved=="Ancestor")
AVB<-rbind(nq,con,anc)
hybrid <-AVB %>% filter(Specie=="3Hybrid")
hybrid_noNQO <- hybrid %>% filter(Evolved!="Evolved_NQO")
hybrid_NQO <- hybrid %>% filter(Evolved=="Evolved_NQO")
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=10)
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=25)
nohybrid <-AVB %>% filter(Specie!="3Hybrid")
AVB <- rbind(nohybrid,hybrid_noNQO,hybrid_NQO)
#Define some functions
my_comparisons <- list( c("1.1Scer", "2.1Scer"), c("1.3Hybrid","2.3Hybrid"),
c("1.2Spar", "2.2Spar"))
cond.labs <- c(
`Evolved_control` = "Evolved in control",
`Evolved_NQO` = "Evolved in UV mimetic",
`Ancestor` = "Ancestor"
)
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
my_comparisons <- list( c("1Scer", "2Spar"), c("1Scer","3Hybrid"),
c("2Spar", "3Hybrid"))
legend_title <- " "
Fig_extD <- AVB %>% dplyr::filter(day==2)%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in control conditions") +
scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(legend_title,values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
theme_prism() +
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.position="top",legend.text.align = 0) +
guides(color = guide_legend(title = "Genotype")) +
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=25,angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=0.1,
legend.text = element_text(size=15),
legend.direction="horizontal")+ylim(0,1)
legfitness <- get_legend(Fig_ext1)
AVB1<-AVB %>%dplyr::filter(Evolved=="Evolved_NQO")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
daytwo <- AVB %>% dplyr::filter(day==2)
Fig_ext1_D <- daytwo %>% dplyr::filter(Evolved=="Evolved_NQO")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in control conditions") +
scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.77, yend=0.77)) +
geom_segment(aes(x=2, xend=3, y=0.80, yend=0.80)) +
geom_segment(aes(x=1, xend=3, y=0.84, yend=0.84)) +
annotate("text", x = 1, y = 0.88, size = 3,
label = c("p < 0.001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.79, 0.82, 0.86),
x = c(1.5, 2.5, 2),
label = c("p < 0.01", "p < 0.01", "p > 0.05"),
family = "", fontface = 3, size=3)+
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+ylim(0,1)
AVB1<-AVB %>%dplyr::filter(Evolved=="Evolved_control")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
Fig_ext1_E <- daytwo %>% dplyr::filter(Evolved=="Evolved_control")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in control conditions") + scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.87, yend=0.87)) +
geom_segment(aes(x=2, xend=3, y=0.90, yend=0.90)) +
geom_segment(aes(x=1, xend=3, y=0.94, yend=0.94)) +
annotate("text", x = 1, y = 0.98, size = 3,
label = c("p < 0.0001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.89, 0.92, 0.96),
x = c(1.5, 2.5, 2),
label = c("p < 0.0001", "p > 0.05", "p < 0.01"),
family = "", fontface = 3, size=3) +
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs)) +
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+ylim(0,1)
AVB1<-AVB %>%dplyr::filter(Evolved=="Ancestor")
AVB1<-AVB1 %>%dplyr::filter(day==2)
AVB1$Specie<-as.factor(AVB1$Specie)
amod <- aov(rval ~ Specie, data = AVB1)
summary(amod)
inter.test1 <- glht(amod, mcp(Specie = "Tukey"))
summary(inter.test1)
cld(inter.test1)
Fig_ext1_F <- daytwo %>% dplyr::filter(Evolved=="Ancestor")%>%
ggplot(aes(x=Specie,y=rval, col=Specie, group=Specie))+
geom_boxplot(outlier.shape=NA,aes(fill=Specie),col="black",alpha=0.2)+
geom_point(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2)+
scale_x_discrete("Genotype", labels=expression(italic(S.cerevisiae), italic(S.paradoxus), Hybrid)) +
xlab("Group") + ylab("Growth rate (OD/hour) \n in control conditions") + scale_color_manual(values=c("green4", "dodgerblue1", "black"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999","grey"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
theme_bw() +
geom_segment(aes(x=1, xend=2, y=0.87, yend=0.87)) +
geom_segment(aes(x=2, xend=3, y=0.90, yend=0.90)) +
geom_segment(aes(x=1, xend=3, y=0.94, yend=0.94)) +
annotate("text", x = 1, y = 0.98, size = 3,
label = c("p < 0.001"),
family = "", fontface = 3)+
annotate("text",
y = c(0.89, 0.92, 0.96),
x = c(1.5, 2.5, 2),
label = c("p < 0.001", "p < 0.01", "p > 0.5"),
family = "", fontface = 3, size=3) +
guides(color = guide_legend(title = "Genotype")) +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
facet_grid(.~Evolved, labeller = as_labeller(cond.labs))+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
guides(fill=guide_legend(title=" "))+
theme(axis.title = element_text(size=14, face = "bold"),
strip.background = element_blank(),
legend.title = element_text(size=12),
legend.text = element_text(size=12),
axis.title.x = element_blank(),
legend.position="none",
strip.text = element_text(face = "bold", size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12)) +ylim(0,1)
#################
############Assemble and save Figure Supplementary 2############
Fig_Supp2<-plot_grid(Fig_ext1_C,Fig_ext1_B,Fig_ext1_A,
Fig_ext1_F,Fig_ext1_E,Fig_ext1_D,
nrow=2)
Fig_Supp2leg <- plot_grid(legfitness, Fig_Supp2, nrow=2,rel_heights = c(2,20))
ggsave (plot = Fig_Supp2leg, filename = "Supplementary_Fig2_low_quality.jpg", units = "cm", device = "jpg",width =40, height =25, dpi = 300, bg = "white")
ggsave (plot = Fig_Supp2leg, filename = "Supplementary_Fig2.png", units = "cm", device = "png",width =40, height =25, dpi = 1000, bg = "white")
ggsave (plot = Fig_Supp2leg, filename = "Supplementary_Fig2.jpg", units = "cm", device = "jpg",width =40, height =25, dpi = 1000, bg = "white")
ggsave (plot = Fig_Supp2leg, filename = "Supplementary_Fig2.svg", units = "cm", device = "svg",width =40, height =25, dpi = 1000, bg = "white")
ggsave (plot = Fig_Supp2leg, filename = "Supplementary_Fig2.pdf", units = "cm", device = "pdf",width =40, height =25, dpi = 1000, bg = "white")
#################
############Figure Figure Supplementary 3############
#Make some functions
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
data <- data.frame(
specie = c(rep("1Scer", 17), rep("2Spar", 18), rep("3Hybrid", 12)),
evolved = c(rep("Ancestor", 5), rep("Evolved_control", 5), rep("Evolved_UV", 7),
rep("Ancestor", 4), rep("Evolved_control", 5), rep("Evolved_NQO", 9),
rep("Ancestor", 3), rep("Evolved_control", 5), rep("Evolved_NQO", 4)),
replicate = c(3, 5, 11, 12, 19, 1, 3, 5, 11, 18, 3, 5, 11, 18, 21, 13,
1, 2, 4, 23, 1, 1, 4, 23, 26, 2, 2,5, 10, 11, 16, 17, 23, 24, 28,
23, 24, 27, 20, 23, 26, 27, 28, 1, 26, 27, 28)
)
data <- data.frame(
specie = rep("1Scer", 25),
evolved = rep("Ancestor", 25),
ploidy = rep("Diploid", 25),
line = c(1, 2, 4, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
)
data <- data.frame(
ploidy = c(rep("Polyploid", 5), rep("Diploid", 25),
rep("Polyploid", 5), rep("Diploid", 25),
rep("Polyploid", 6), rep("Diploid", 24),
rep("Polyploid", 4), rep("Diploid", 26),
rep("Polyploid", 5), rep("Diploid", 25),
rep("Polyploid", 6), rep("Diploid", 24),
rep("Polyploid", 3), rep("Diploid", 27),
rep("Polyploid", 5), rep("Diploid", 25),
rep("Polyploid", 4), rep("Diploid", 26)),
specie = rep(c("1Scer", "2Spar", "3Hybrid"), each = 90),
evolved = c(rep("Ancestor", 30), rep("Evolved_control", 30), rep("Evolved_NQO", 30),
rep("Ancestor", 30), rep("Evolved_control", 30), rep("Evolved_NQO", 30),
rep("Ancestor", 30), rep("Evolved_control", 30), rep("Evolved_NQO", 30)),
line = c(3, 5, 11, 12, 19,
1, 2, 4, 6, 7, 8, 9, 10, 13,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,
1, 3, 5, 11, 18,
2, 4, 6, 7, 8, 9, 10, 12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,
3, 5, 11,13,18, 21,
1,2, 4,6, 7, 8, 9, 10, 12,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,
1, 2, 4, 23,
3,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29,30,
1, 2, 4, 23, 26,
3,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,27,28,29,30,
2, 5, 16, 23, 24, 28,
1,3,4,6,7,8,9,10,11,12,13,14,15,17,18,19,20,21,22,25,26,27,29,30,
23, 24, 27,
1, 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,29,21,22,25,26,28,29,30,
20, 23, 26, 27, 28,
1, 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,22,24,25,29,30,
1, 26, 27, 28,
2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,29,30))
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
data_nohyb <- filter(data, specie!="3Hybrid")
data_hyb <- filter(data, specie=="3Hybrid")
data_hyb_nonq <- filter(data_hyb, evolved!="Evolved_NQO")
data_hyb_nq <- filter(data_hyb, evolved=="Evolved_NQO")
data_hyb_nq <- filter(data_hyb_nq, line!=10)
data_hyb_nq <- filter(data_hyb_nq, line!=25)
data<-rbind(data_nohyb,data_hyb_nonq,data_hyb_nq)
count <- data %>%
dplyr::group_by(specie, evolved,ploidy) %>%
dplyr::summarise(n_lines = n())
Fig3A <- count %>%
filter(evolved != "Ancestor") %>%
ggplot(aes(x = reorder(interaction(specie, evolved, ploidy)), y = n_lines, fill = specie)) +
geom_bar_pattern(
stat = 'identity',
width = 0.9,
color = "black",
alpha = 0.7,
aes(pattern = evolved),
pattern_fill = "black",
pattern_angle = 45,
pattern_density = 0.1,
pattern_spacing = 0.05,
pattern_alpha = 0.25
) +
scale_pattern_manual(" ",
values = c("Evolved_control" = "none", "Evolved_NQO" = "stripe"),
labels = c("Evolved_control" = "Evolved in control", "Evolved_NQO" = "Evolved in UV mimetic")) +
scale_fill_manual(" ", values = c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
xlab("Ploidy") +
ylab("Count") +
theme_light() +
background_grid(
major = c("xy"),
minor = c("y"),
size.major = 0.5,
size.minor = 0.3,
color.major = "grey85",
color.minor = "grey85"
) +
scale_x_discrete(labels = c("Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid")) +
geom_text(aes(label = n_lines), position = position_stack(vjust = 0.5)) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 12, color = "black"),
axis.text.y = element_text(size = 12, color = "black"),
axis.title = element_text(size = 13, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
legend.position = "top",
legend.direction = "horizontal",
legend.title = element_blank(),
legend.box = "vertical",
legend.text = element_text(size=12),
legend.key.width = unit(1, "cm")
) +
guides(fill = guide_legend(override.aes = list(pattern = "none")),
pattern = guide_legend(override.aes = list(fill = NA))) + # Asegurarse de que la leyenda de `pattern` no interfiera con el `fill`
geom_vline(xintercept = c(4.5, 8.5))
legheatmap0 <- get_legend(Fig3A)
Fig3A <- count %>%
filter(evolved != "Ancestor") %>%
ggplot(aes(x = reorder(interaction(specie, evolved, ploidy)), y = n_lines, fill = specie)) +
geom_bar_pattern(
stat = 'identity',
width = 0.9,
color = "black",
alpha = 0.7,
aes(pattern = evolved),
pattern_fill = "black",
pattern_angle = 45,
pattern_density = 0.1,
pattern_spacing = 0.05,
pattern_alpha = 0.25
) +
scale_pattern_manual(" ",
values = c("Evolved_control" = "none", "Evolved_NQO" = "stripe"),
labels = c("Evolved_control" = "Evolved in control", "Evolved_NQO" = "Evolved in UV mimetic")) +
scale_fill_manual(" ", values = c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
xlab(" ") +
ylab("Count") +
theme_light() +
background_grid(
major = c("xy"),
minor = c("y"),
size.major = 0.5,
size.minor = 0.3,
color.major = "grey85",
color.minor = "grey85"
) +
scale_x_discrete(labels = c("Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid",
"Diploid", "Polyploid")) +
geom_text(aes(label = n_lines), position = position_stack(vjust = 0.5)) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 12, color = "black"),
axis.text.y = element_text(size = 12, color = "black"),
axis.title = element_text(size = 13, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
legend.position = "none") +
guides(fill = guide_legend(override.aes = list(pattern = "none")),
pattern = guide_legend(override.aes = list(fill = NA))) + # Asegurarse de que la leyenda de `pattern` no interfiera con el `fill`
geom_vline(xintercept = c(4.5, 8.5))
Ext2_A<-plot_grid(legheatmap0,Fig3A,nrow=2,rel_heights = c(0.5,2))
#related to fitness
genomics1 <- dplyr:::filter(Supp_genomics,condition=="NQO")
genomics1 <- dplyr:::filter(genomics1,day==2)
genomics1$Replicate<-as.numeric(genomics1$Replicate)
genomics2<-genomics1 %>% dplyr:::filter(Evolved=="Evolved_NQO")
genomics1 <- dplyr:::filter(Supp_genomics,condition=="NQO")
genomics1 <- dplyr:::filter(genomics1,day==2)
genomics1$Replicate<-as.numeric(genomics1$Replicate)
genomics3<-genomics1 %>% dplyr:::filter(Evolved=="Ancestor")
genomics4<- left_join(genomics2,genomics3, by=c("Specie"="Specie","condition"="condition","Replicate"="Replicate"))
genomics4$fitness_gain<- (genomics4$rval.x - genomics4$rval.y) / genomics4$rval.y
genomics4$percentage <- genomics4$fitness_gain * 100
data1<- data %>% filter(evolved=="Evolved_NQO")
data2<- full_join(data1,genomics4,by=c("line"="Replicate","specie"="Specie"))
data2 <- na.omit(data2)
toexpr<-function(x) {
getfun <- function(x) {
ifelse(x=="Hybrid", "plain", "italic")
}
as.expression(unname(Map(function(f,v) substitute(f(v), list(f=as.name(f), v=as.character(v))), getfun(x), x)))
}
Fig_Fitness <- data2 %>%
ggplot(aes(x = interaction(ploidy,specie), y = percentage, fill = specie)) +
geom_boxplot() +
geom_jitter(width = 0.2, alpha = 0.5) +
scale_fill_manual(" ", values = c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
xlab("Species and Ploidy") +
ylab("Fitness") +
theme_light() +
background_grid(
major = c("xy"),
minor = c("y"),
size.major = 0.5,
size.minor = 0.3,
color.major = "grey85",