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Bautista2024_Supplementary.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("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)
#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("pegas")
library(pegas)
#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)
#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_ploidy <- read.csv("2Supp_ploidy.csv",stringsAsFactors = F)
Supp_ploidy_heatmap <- read.csv("2Supp_ploidy_heatmap.csv")
Supp_data_aneuploidies<- read.csv("3Supp_data_aneuploidies.csv")
Supp_genomics <- read_csv("4Supp_growth.csv")
Supp_Table_counts_aneuploidies <- read.csv("4Supp_Table_counts_aneuploidies.csv")
Supp_Aneuploidy_chrom <- read_excel("4Supp_Aneuploidy_chrom.xlsx")
Supp_new_genome3c_new<-read.csv("4Supp_new_genome3c_new.csv")
Supp4_GO <- image_read_pdf("5Supp_GO.pdf")
Supp_raw_cov<-read.csv("6Supp_raw_cov.csv")
Supp_growth_plasmids<-read.csv("7Supp_growth_plasmids.csv")
Supp_growth_curves_plasmids<-read.csv("7Supp_growth_curves_plasmids.csv")
Supp_growth_plasmids2<-read.csv("7Supp_growth_plasmids2.csv")
Supp_growth_curves_plasmids2<-read.csv("7Supp_growth_curves_plasmids2.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$library <- "Sample"
hybrid <-Supp_coverage %>% filter(Strain=="3Hybrid")
hybrid_noNQO <- hybrid %>% filter(Type!="Evolved_NQO")
hybrid_NQO <- hybrid %>% filter(Type=="Evolved_NQO")
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=10)
hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=25)
#We kept them in order to calculate the mean:
#hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=1)
#hybrid_NQO <- hybrid_NQO %>% filter(Replicate!=21)
nohybrid <-Supp_coverage %>% filter(Strain!="3Hybrid")
joined <- rbind(nohybrid,hybrid_noNQO,hybrid_NQO)
joined$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############
#List strains with whole ploidy shifts
#make graphs of everything
#Ploidy graphs
#Individual plots of ploidy
ploidy <- Supp_ploidy
x<-ploidy%>%filter(!(plate=="A"&evolved=="4n"))
x<-x%>%filter(!(plate=="C"&evolved=="4n"))
x<-x%>%filter(!(plate=="B"&evolved=="2n"&replicate=="Control"&other_info=="WT ploidy2"))
x<-x%>%filter(!(plate=="B"&evolved=="2n"&replicate=="Control"))
x<-x%>%filter(!(plate=="A"&evolved=="2n"&replicate=="Control"))
x<-x%>%filter(!(plate=="C"&evolved=="2n"&specie=="Spar haploid nat alfa"))
x<-x%>%filter(!(plate=="A"&evolved=="3n"))
x<-x%>%filter(!(plate=="C"&evolved=="3n"))
x<-x%>%filter(!(plate=="A"&evolved=="n"))
x<-x%>%filter(!(plate=="B"&evolved=="n"))
x<-x%>%filter(!(plate=="C"&evolved=="n"&specie=="Spar haploid hyg alfa"))
fdata <- x
newdat53n <- filter(newdat5, LMH>184)
triploides <- newdat53n %>% group_by(evolved,same_Replicate,specie) %>% dplyr:::summarise(n()) %>% ungroup()
colnames(newdat)[5] <- "counts"
control3n<-filter(x, evolved=="3n")
control2n<-filter(x, evolved=="2n")
controles <- filter(x, evolved=="2n" | evolved=="3n")
fdata$log_FL1A = log1p(fdata$FL1_A)
control2n$log_FL1A = log1p(control2n$FL1_A)
control3n$log_FL1A = log1p(control3n$FL1_A)
legend_title <- "Sample"
conto <- Supp_ploidy_heatmap %>% filter(replicate=="Control")
neworder <- c("n","2n","3n","4n")
iris2 <- arrange(transform(conto,
evolved=factor(evolved,levels=neworder)),evolved)
legend_title <- "Cell count (Density)"
#Graphs control
con2<- iris2 %>% filter(evolved=="2n") %>% ggplot(aes(LMH, evolved))+
geom_tile(aes(fill = counts)) + scale_fill_gradient(legend_title,low = 'white', high = 'darkblue',limits=c(0,800)) +
ylab("Samples") +
xlab("Fluorescence")+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))+
theme(axis.text = element_text(face="bold"))+
theme(axis.line=element_blank())+
theme(strip.text = element_text(face = "bold", size = 12),
strip.background = element_blank()) +
ylab("Controls")+
xlab("DNA content (Fluorescence)")+
theme(axis.text.y=element_text(size=5),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme(legend.position="none")+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
guides(fill = guide_colorbar(title.position = "top"))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
xlim(300, 500)+
geom_vline(xintercept = c(392, 420), linetype = "dashed", color = "black",size=0.5)
con3<- iris2 %>%filter(evolved=="2n") %>% ggplot(aes(LMH, evolved))+
geom_tile(aes(fill = counts)) + scale_fill_gradient(legend_title,low = 'white', high = 'darkblue',limits=c(0,800)) +
ylab("Samples") +
xlab("Fluorescence")+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))+
theme(axis.line=element_blank())+
theme(axis.text = element_text(face="bold"))+
theme(strip.text = element_text(face = "bold", size = 12),
strip.background = element_blank()) +
xlab("DNA content (Fluorescence)")+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.x=element_blank(),
axis.ticks.y=element_blank())+
theme(legend.position="none")+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
guides(fill = guide_colorbar(title.position = "top"))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
xlim(300, 500)+
geom_vline(xintercept = c(392, 420), linetype = "dashed", color = "black",size=0.5)
newdat$replicate<-as.factor(newdat$replicate)
legend_title <- "Cell count (Density)"
cond.labs <- c(
`Evolved_control` = "Evolved in control",
`Evolved_NQO` = "Evolved in UV mimetic",
`Ancestor` = "Ancestor",
`1Scer` = "S. cerevisiae",
`2Spar` = "S. paradoxus",
`3Hybrid` = "Hybrid"
)
Supp_ploidy_heatmap<-Supp_ploidy_heatmap %>% filter(replicate!="Control")
Supp_ploidy_heatmap$specie <- factor(Supp_ploidy_heatmap$specie,
labels = c("bolditalic(S.cerevisiae)",
"bolditalic(S.paradoxus)",
"bold(Hybrid)"))
Supp_ploidy_heatmap$evolved <- factor(Supp_ploidy_heatmap$evolved,
labels = c("bold(Ancestor)",
"bold(Control)",
"bold(UV_mimetic)"))
Supp_ploidy_heatmap <- Supp_ploidy_heatmap %>%
filter(!(evolved == "bold(UV_mimetic)" & same_rep == "3Hybrid_25"))
Supp_ploidy_heatmap <- Supp_ploidy_heatmap %>%
filter(!(evolved == "bold(UV_mimetic)" & same_rep == "3Hybrid_10"))
Samples_heatmap_leg <- Supp_ploidy_heatmap %>%
ggplot(aes(LMH, replicate))+
geom_tile(aes(fill = counts)) + scale_fill_gradient(legend_title,low = 'white', high = 'darkblue') +
ylab("Samples") +
xlab("Fluorescence")+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 12),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_text(size=5),
axis.ticks.x=element_blank(),
axis.ticks.y=element_blank())+
theme(legend.title = element_text(angle = 0, vjust= 0.3,hjust= 0.01),legend.title.align=0.5,
legend.box.just = "center",
legend.direction = "vertical",
legend.position = "right")+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
guides(fill = guide_colorbar(title.position = "top"))+
theme(plot.title = element_text(hjust = 0.8, size=12, face = "bold"))+
facet_grid(evolved~specie, scales= "free", space = "free",labeller=(label_parsed))+
xlim(300, 500)
legheatmap <- get_legend(Samples_heatmap_leg)
Samples_heatmap <- Supp_ploidy_heatmap %>% filter(replicate!="Control") %>% ggplot(aes(LMH, replicate))+#,label = interaction(specie,info,glycerol), position = "identity")) +
geom_tile(aes(fill = counts)) + scale_fill_gradient(legend_title,low = 'white', high = 'darkblue') +
ylab("Samples") +
xlab("Fluorescence")+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)+
theme(axis.text = element_text(face="bold"))+
theme(strip.text = element_text(face = "bold", size = 12),
strip.background = element_blank()) +
xlab("DNA content (A.U. Fluorescence)")+
theme(axis.text.y=element_text(size=5))+
theme(legend.position="none")+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
guides(fill = guide_colorbar(title.position = "top"))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
scale_y_discrete(name ="Line",
breaks=c("1","2","3","4","5","6",
"7","8","9","10","11",
"12","13","14","15","16",
"17","18","19","20","21",
"22","23","24","25","26",
"27","28","29","30"),
limits=c("1","2","3","4","5","6",
"7","8","9","10","11",
"12","13","14","15","16",
"17","18","19","20","21",
"22","23","24","25","26",
"27","28","29","30"),
label=c("1","2","3","4","5","6",
"7","8","9","10","11",
"12","13","14","15","16",
"17","18","19","20","21",
"22","23","24","25","26",
"27","28","29","30"))+
facet_grid(evolved~specie, scales= "free", space = "free",labeller=label_parsed)+
xlim(300, 500)+
geom_vline(xintercept = c(392, 420), linetype = "dashed", color = "black",size=0.5)
Samples_heatmap2 <- Samples_heatmap + theme(panel.spacing.x = unit(0.52, "cm"))
#################
############Assemble and save Supplementary Figure 2############
leg <- plot_grid(con2,con3,con3, ncol = 4,rel_widths = c(13.6,12.2,12,1.6))
Fig2SuppA <- plot_grid(leg,Samples_heatmap2, nrow = 2, rel_heights = c(0.8,10))
Fig2SuppAleg <- plot_grid(Fig2SuppA,legheatmap, nrow = 1, rel_widths = c(10,2))
ggsave (plot = Fig2SuppAleg, filename = "Supplementary_Fig2_low_quality.jpg", units = "cm", device = "jpg",width = 25, height = 25, dpi = 300,bg = "white")
ggsave (plot = Fig2SuppAleg, filename = "Supplementary_Fig2.png", units = "cm", device = "png",width = 25, height =25, dpi = 1000,bg = "white")
ggsave (plot = Fig2SuppAleg, filename = "Supplementary_Fig2.jpg", units = "cm", device = "jpg",width = 25, height = 25, dpi = 1000,bg = "white")
ggsave (plot = Fig2SuppAleg, filename = "Supplementary_Fig2.svg", units = "cm", device = "svg",width = 25, height =25, dpi = 1000,bg = "white")
ggsave (plot = Fig2SuppAleg, filename = "Supplementary_Fig2.pdf", units = "cm", device = "pdf",width = 25, height =25, dpi = 1000,bg = "white")
#################
############Figure Supplementary 3############
###Boxplot aneuploidies
#Define some elements for the graph
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)))
}
#Fisher test
scer <- dplyr:::filter(Supp_Table_counts_aneuploidies,Strain=="1Scer")
spar <- dplyr:::filter(Supp_Table_counts_aneuploidies,Strain=="2Spar")
hyb <- dplyr:::filter(Supp_Table_counts_aneuploidies,Strain=="3Hybrid")
#For S.cerevisiae
# With aneu Without aneu
#control 0 29
#NQO 3 26
m_scer <- matrix(c(0,3,29,26),nrow = 2,ncol=2,byrow=FALSE)
fisher.test(m_scer)
#p-value: 0.2368
#For S.paradoxus
# With aneu Without aneu
#control 0 30
#NQO 6 24
m_spar <- matrix(c(0,6,30,24),nrow = 2,ncol=2,byrow=FALSE)
fisher.test(m_spar)
#p-value: 0.02372
#For Hybrid
# With aneu Without aneu
#control 1 25
#NQO 4 22
m_hyb <- matrix(c(1,4,25,22),nrow = 2,ncol=2,byrow=FALSE)
fisher.test(m_hyb)
#p-value: 0.3497
B_Table_counts_aneuploidies_filtered <- Supp_Table_counts_aneuploidies %>%
dplyr:::filter(!(Strain=="1Scer" & Replicate==16))
B_Table_counts_aneuploidies_filtered <- B_Table_counts_aneuploidies_filtered %>%
dplyr:::filter(!(Strain=="3Hybrid" & Replicate==1))
B_Table_counts_aneuploidies_filtered <- B_Table_counts_aneuploidies_filtered %>%
dplyr:::filter(!(Strain=="3Hybrid" & Replicate==10))
B_Table_counts_aneuploidies_filtered <- B_Table_counts_aneuploidies_filtered %>%
dplyr:::filter(!(Strain=="3Hybrid" & Replicate==21))
B_Table_counts_aneuploidies_filtered <- B_Table_counts_aneuploidies_filtered %>%
dplyr:::filter(!(Strain=="3Hybrid" & Replicate==25))
FigSupp3 <- ggplot(B_Table_counts_aneuploidies_filtered, aes(x = interaction(Type,Strain), y = counts,fill=Strain))+
geom_boxplot(outlier.shape = NA,aes(fill=as.factor(Strain)))+
geom_jitter(colour="black",pch=21,height = 0, size=3,alpha=0.7, aes(fill=as.factor(Strain)))+
ylab("Number of \n aneuploidies / line")+
xlab("Genotype")+
scale_fill_manual(values=c("green4", "dodgerblue1", "#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus", "Hybrid"))) +
scale_x_discrete("", labels=c("Evolved in \n control","Evolved in \n UV mimetic",
"Evolved in \n control","Evolved in \n UV mimetic",
"Evolved in \n control","Evolved in \n UV mimetic"))+
theme_bw(base_size=24) +
geom_segment(aes(x=3, xend=4, y=1.3, yend=1.3)) +
annotate("text",
y = c(1.45),
x = c(3.5),
label = c("p < 0.05"),
family = "", fontface = 3, size=4) +
scale_y_continuous(breaks = c(0, 1, 2))+
theme_bw(base_size=24) +
theme(strip.background = element_blank(),
panel.background = element_blank(),
strip.text.x = element_blank(),
plot.title = element_text(size=15, hjust=0, face = "bold", color="black"),
axis.text.x = element_text(size = 11, color="black",face = "bold"),
axis.text.y = element_text(size = 13, color="black"),
axis.title = element_text(size = 15,face="bold"),
legend.position = "top",
legend.direction = "horizontal",
legend.text = element_text(size=14),
legend.title = element_text(size=14),
legend.margin = margin(-10, -10,-6,-2))+
guides(fill = guide_legend(title = " "))
#################
############Assemble and save Supplementary Figure 3############
ggsave (plot = FigSupp3, filename = "Supplementary_Fig3_low_quality.jpg", units = "cm", device = "jpg",width = 25, height = 10, dpi = 300,bg = "white")
ggsave (plot = FigSupp3, filename = "Supplementary_Fig3.png", units = "cm", device = "png",width = 25, height =10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp3, filename = "Supplementary_Fig3.jpg", units = "cm", device = "jpg",width = 25, height = 10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp3, filename = "Supplementary_Fig3.svg", units = "cm", device = "svg",width = 25, height =10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp3, filename = "Supplementary_Fig3.pdf", units = "cm", device = "pdf",width = 25, height =10, dpi = 1000,bg = "white")
#################
############Figure Supplementary 4############
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
Supp_Table_counts_aneuploidies_NQO <- Supp_Table_counts_aneuploidies %>%
dplyr:::filter(Type=="Evolved_NQO")
every_aneu_NQO<- right_join(Supp_Aneuploidy_chrom,Supp_Table_counts_aneuploidies_NQO, by=c("Specie"="Strain",
"Replicate"="Replicate"))
FITNESS_LOH <-left_join(every_aneu_NQO,genomics4,by=c("Specie"="Specie","Replicate"="Replicate"))
#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)))
}
NQO <- dplyr:::filter(FITNESS_LOH, Type=="Evolved_NQO")
#Get statistics
NQO <- dplyr:::filter(FITNESS_LOH, Type=="Evolved_NQO")
NQO_scer <- dplyr:::filter(NQO, Specie=="1Scer")
ggscatter(NQO_scer, x = "counts", y = "percentage",
color = "black", shape = 21, size = 3, # Points color, shape and size
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "spearman", label.x = 0.25, label.sep = "\n"))
NQO_spar <- dplyr:::filter(NQO, Specie=="2Spar")
ggscatter(NQO_spar, x = "counts", y = "percentage",
color = "black", shape = 21, size = 3, # Points color, shape and size
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "spearman", label.x = 0.25, label.sep = "\n"))
NQO_hyb <- dplyr:::filter(NQO, Specie=="3Hybrid")
ggscatter(NQO_hyb, x = "counts", y = "percentage",
color = "black", shape = 21, size = 3, # Points color, shape and size
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "spearman", label.x = 0.25, label.sep = "\n"))
legend_title <- " "
FigSupp4 <- FITNESS_LOH %>% filter(Type=="Evolved_NQO")%>%
ggplot(aes(x=as.factor(counts),y = percentage))+
stat_smooth(method="lm", alpha=0.1,aes(group=Specie,col=Specie))+
geom_jitter(pch=21, aes(fill=Specie), col="black", show.legend = F, alpha=0.5,size=2,width = 0.1, height = 0.1)+
xlab("Number of aneuploidies / line")+ylab("Increase in growth rate (%)")+
border() +
scale_fill_manual(legend_title,values=c("green4","dodgerblue1","#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus","Hybrid"))) +
scale_color_manual(legend_title,values=c("green4","dodgerblue1","#FF9999"),
labels = toexpr(c("S. cerevisiae", "S. paradoxus","Hybrid"))) +
theme(legend.position="none")+
stat_smooth(method="lm", alpha=0.1)+
theme_prism()+
theme_bw(base_size=24) +
theme(axis.title = element_text(size=12, face = "bold"),
strip.text = element_text(face = "bold", size = 9),
strip.background = element_blank(),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=10),
panel.background = element_blank(),
legend.text = element_text(size=8),
legend.title = element_text(size=9),
legend.position = "top",
legend.direction = "horizontal",
legend.margin = margin(-15, -15,-15,-15))+
annotate("text",
y = 190,x = 3,
label = c("rs = -0.18, p > 0.05"),
family = "", fontface = 3, size=3, col="darkgreen") +
annotate("text",
y = 180,x = 3,
label = c("rs = -0.22, p > 0.05"),
family = "", fontface = 3, size=3, col="dodgerblue3") +
annotate("text",
y = 170,x = 3,
label = c("rs = -0.27, p > 0.05"),
family = "", fontface = 3, size=3, col="lightpink4")+
stat_smooth(method="lm", alpha=0.2)
#################
############Assemble and save Supplementary Figure 4############
ggsave (plot = FigSupp4, filename = "Supplementary_Fig4_low_quality.jpg", units = "cm", device = "jpg",width = 15, height =10, dpi = 300,bg = "white")
ggsave (plot = FigSupp4, filename = "Supplementary_Fig4.png", units = "cm", device = "png",width = 15, height =10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp4, filename = "Supplementary_Fig4.jpg", units = "cm", device = "jpg",width = 15, height =10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp4, filename = "Supplementary_Fig4.svg", units = "cm", device = "svg",width = 15, height =10, dpi = 1000,bg = "white")
ggsave (plot = FigSupp4, filename = "Supplementary_Fig4.pdf", units = "cm", device = "pdf",width = 15, height =10, dpi = 1000,bg = "white")
#################
############Figure Supplementary 5############
#Special Packages of enrichment
#install.packages("AnnotationDbi")
library(AnnotationDbi)
#BiocManager::install("AnnotationHub")
library(AnnotationHub)
#install.packages("biomaRt")
library(biomaRt)
#install.packages("biomartr")
library(biomartr)
#BiocManager::install("clusterProfiler")
library(clusterProfiler)
#BiocManager::install("cola")
library(cola)
#BiocManager::install("DO.db")
library(DO.db)
#install.packages("DOSE")
library(DOSE)
#install.packages("enrichplot")
library(enrichplot)
#BiocManager::install("gage")
library(gage)
#install.packages("gageData")
library(gageData)
#install.packages("ggnewscale")
library(ggnewscale)
#install.packages("gsEasy")
library(gsEasy)
#BiocManager::install("hu6800.db")
library(hu6800.db)
#BiocManager::install("org.Sc.sgd.db")
library(org.Sc.sgd.db)
#BiocManager::install("ReactomePA")
library(ReactomePA)
#BiocManager::install("simplifyEnrichment")
library(simplifyEnrichment)
m1 <- read.csv("5Supp_GO_scer_NQO_without_outliers.csv")
m1_miss <- filter(m1, Consequence=="missense_variant")
m1 <- m1_miss
m3 <- read.csv("5Supp_GO_spar_NQO_without_outliers.csv")
m3_miss <- filter(m3, Consequence=="missense_variant")
m3 <- m3_miss
m5 <- read.csv("5Supp_GO_hybrid_NQO_without_outliers_hybrid_concatenated.csv")
m5_miss <- filter(m5, Consequence=="missense_variant")
m5 <- m5_miss
#Remove 25
m5 <- filter(m5,Replicate!=25)
m1a<- m1$ensembl_gene_id
m1a<-as.data.frame(m1a)
colnames(m1a) <- c("gene")
m1a<- m1$ensembl_gene_id
hyb_NQO <- enrichGO(m1a,"org.Sc.sgd.db", keyType = "ENSEMBL",
ont = "ALL",
qvalueCutoff = 0.5,pvalueCutoff=0.5)
x1 <- pairwise_termsim(hyb_NQO)
set.seed(123)
A1<- emapplot(x1,color = "p.adjust",group_category = T, group_legend = T, cex_label_group = 1.5, node_label = "ONTOLOGY",nCluster = 5)+
ggtitle("Scer 4-NQO")
m3a<- m3$ensembl_gene_id
m3a<-as.data.frame(m3a)
colnames(m3a) <- c("gene")
m3a<- m3$ensembl_gene_id
hyb_NQO <- enrichGO(m3a,"org.Sc.sgd.db", keyType = "ENSEMBL",
ont = "ALL",
qvalueCutoff = 0.5,pvalueCutoff=0.5)
x2 <- pairwise_termsim(hyb_NQO)
set.seed(123)
A2<- emapplot(x2,color = "p.adjust",group_category = T, group_legend = T, cex_label_group = 1.5, node_label = "ONTOLOGY",nCluster = 5)+
ggtitle("Spar 4-NQO")
m5a<- m5$ensembl_gene_id
m5a<-as.data.frame(m5a)
colnames(m5a) <- c("gene")
m5a<- m5$ensembl_gene_id
hyb_NQO <- enrichGO(m5a,"org.Sc.sgd.db", keyType = "ENSEMBL",
ont = "ALL",
qvalueCutoff = 0.5,pvalueCutoff=0.5)
x3 <- pairwise_termsim(hyb_NQO)
set.seed(123)
A3<- emapplot(x3,color = "p.adjust",group_category = T, group_legend = T, cex_label_group = 1.5, node_label = "ONTOLOGY",nCluster = 5)+
ggtitle("Hybrid 4-NQO")
network<- plot_grid(A1,A2,A3, nrow=1)
#################
############Assemble and save raw Supplementary Figure 5############
ggsave (plot = network, filename = "Supplementary_Fig5_raw_low_quality.jpg", units = "cm", device = "jpg",width = 60, height =25, dpi = 400,bg = "white")
ggsave (plot = network, filename = "Supplementary_Fig5_raw.png", units = "cm", device = "png",width = 60, height =25, dpi = 1000,bg = "white")
ggsave (plot = network, filename = "Supplementary_Fig5_raw.jpg", units = "cm", device = "jpg",width = 60, height =25, dpi = 1000,bg = "white")
ggsave (plot = network, filename = "Supplementary_Fig5_raw.svg", units = "cm", device = "svg",width = 60, height =25, dpi = 1000,bg = "white")
ggsave (plot = network, filename = "Supplementary_Fig5_raw.pdf", units = "cm", device = "pdf",width = 60, height =25, dpi = 1000,bg = "white")
#################
#Calculate ratio of enrichment
x1b<-as.data.frame(x1)
x1b$Specie<-"Scer"
x2b<-as.data.frame(x2)
x2b$Specie<-"Spar"
x3b<-as.data.frame(x3)
x3b$Specie<-"Hybrid"
xb<-rbind(x1b,x2b,x3b)
fraction_a_number <- function(fraction) {
eval(parse(text = fraction))
}
View(xb)
xb$GeneRatio_number <- sapply(xb$GeneRatio, fraction_a_number)
xb$BgRatio_number <- sapply(xb$BgRatio, fraction_a_number)
xb$enrichment_Ratio <- xb$GeneRatio_number / xb$BgRatio_number
#So the most 2 enriched GO term (higher value of this division of ratios) per specie:
#S.cerevisiae: replication-born double-strand break repair via sister chromatid exchange and regulation of cell differentiation
#S.paradoxus: ER-associated misfolded protein catabolic process, endocytic vesicle
#Hybrid: trehalose metabolic process, ABC-type transporter activity (normally involved in efflux pumps poner)
#################
############Assemble and save Supplementary Figure 5############
#We added some aesthetic modifications so we import it
gpp <- rasterGrob(Supp4_GO, interpolate=TRUE)
FigSupp5<-plot_grid(gpp)
ggsave (plot = FigSupp5, filename = "Supplementary_Fig5_low_quality.jpg", units = "cm", device = "jpg",width = 20, height =40, dpi = 300,bg = "white")
ggsave (plot = FigSupp5, filename = "Supplementary_Fig5.png", units = "cm", device = "png",width = 20, height =40, dpi = 1000,bg = "white")
ggsave (plot = FigSupp5, filename = "Supplementary_Fig5.jpg", units = "cm", device = "jpg",width = 20, height =40, dpi = 300,bg = "white")
ggsave (plot = FigSupp5, filename = "Supplementary_Fig5.svg", units = "cm", device = "svg",width = 20, height =40, dpi = 1000,bg = "white")
ggsave (plot = FigSupp5, filename = "Supplementary_Fig5.pdf", units = "cm", device = "pdf",width = 20, height =40, dpi = 300,bg = "white")
#################
############Figure Supplementary 6############
##Panel A
Supp_new_genome3c_new2 <- dplyr:::filter(Supp_new_genome3c_new, Strain=="3Hybrid")
Supp_new_genome3c_new2 <- dplyr:::filter(Supp_new_genome3c_new2, Type=="Evolved_NQO")
exampleHybrid <- dplyr:::filter(Supp_new_genome3c_new2, Strain=="3Hybrid")
exampleHybrid <-dplyr:::filter(exampleHybrid,Replicate!=1)
exampleHybrid <-dplyr:::filter(exampleHybrid,Replicate!=21)
exampleHybrid <-dplyr:::filter(exampleHybrid,Replicate!=10)
exampleHybrid <-dplyr:::filter(exampleHybrid,Replicate!=25)
exampleHybrid<-filter(exampleHybrid,Type=="Evolved_NQO")
nq_snps_HYBRID <- dplyr:::filter(exampleHybrid, Strain=="3Hybrid")
nq_snps_HYBRID<- nq_snps_HYBRID %>% mutate(Normalized_read=if_else(Normalized_read >1.5, 1.5,Normalized_read))
nq_snps_HYBRID<- nq_snps_HYBRID %>% mutate(Normalized_read=if_else(Normalized_read < -1.5, -1.5,Normalized_read))
nq_snps_HYBRID<- filter(nq_snps_HYBRID,Replicate==28)
nq_genome<- nq_snps_HYBRID
legend_title <- "Relative Read Depth"
FigSupp6A <-nq_genome %>% dplyr:::filter(Type=="Evolved_NQO") %>%
ggplot(aes(x = as.numeric(start2),y = as.factor(chrom4),fill=Normalized_read)) +
theme_prism() +
theme(axis.text = element_text(size=20),
axis.title = element_text(size=25))+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
geom_tile(aes(fill =Normalized_read),height=0.65,size=0.2) +
scale_fill_gradient2(legend_title,low = "darkslateblue", mid = "lavender", high = "orange3", midpoint = 0, limits = c(-2.9, 2.9),breaks= c(-2,-1,0,1,2))+
scale_y_discrete(limits=c("32","31","30","29","28","27","26","25","24","23","22","21","20","19","18","17",
"16","15","14","13","12","11","10","9","8","7","6","5","4","3","2","1"),
labels=c("Spar XVI","Scer chrXVI","Spar chrXV","Scer chrXV",
"Spar chrXIV","Scer chrXIV","Spar chrXIII","Scer chrXIII",
"Spar chrXII","Scer chrXII","Spar chrXI","Scer chrXI",
"Spar chrX","Scer chrX","Spar chrIX","Scer chrIX",
"Spar chrVIII","Scer chrVIII","Spar chrVII","Scer chrVII",
"Spar chrVI","Scer chrVI","Spar chrV","Scer chrV",
"Spar chrIV","Scer chrIV","Spar chrIII","Scer chrIII",
"Spar chrII","Scer chrII","Spar chrI","Scer chrI"))+
scale_x_continuous(breaks=c(0,500000,1000000,1500000),
labels=c(0,500,1000,1500))+
facet_wrap(.~ Replicate, ncol=13)+
xlab("Coordinate (kbp)")+
ylab("Chromosome") +
theme(strip.text = element_text(
size = 20))+
theme(plot.title = element_text(size = 30, face = "bold"))+
theme(legend.text = element_text(size = 30))+
theme(legend.background = element_rect(fill="snow2",
size=0.4, linetype="solid",colour ="grey"))+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=0.1)+
theme(legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=10),
legend.text = element_text(size=10))+
theme_bw() +
theme(panel.spacing = unit(0.72, "cm"))+
theme(legend.position="bottom")+
theme(axis.title = element_text(size=14, face = "bold"),
strip.text =element_blank(),
strip.background = element_blank(),
axis.text.x = element_text(vjust = 0.5, hjust=1))
#Panel B
aneuploidy<-Supp_raw_cov
aneuploidy$Replicate<-as.numeric(aneuploidy$Replicate)
all_mean_genome_chrom<-aneuploidy %<>% mutate(chrom2 = ifelse(chrom=="utg351_pilon","Spar chrI",
ifelse(chrom=="utg1271_pilon","Spar chrII",
ifelse(chrom=="utg584_pilon","Spar chrIII",
ifelse(chrom=="utg988_pilon","Spar chrIV",
ifelse(chrom=="utg69_pilon","Spar chrV",
ifelse(chrom=="utg639_pilon","Spar chrVI",
ifelse(chrom=="utg199_pilon","Spar chrVII",
ifelse(chrom=="utg176_pilon","Spar chrVIII",
ifelse(chrom=="utg1121_pilon","Spar chrIX",
ifelse(chrom=="utg245_pilon","Spar chrX",
ifelse(chrom=="utg298_pilon","Spar chrXI",
ifelse(chrom=="utg110_pilon","Spar chrXII 1/2",
ifelse(chrom=="utg11_pilon","Spar chrXII 2/2",
ifelse(chrom=="utg210_pilon","Spar chrXIII",
ifelse(chrom=="utg48_pilon","Spar chrXIV",
ifelse(chrom=="utg155_pilon","Spar chrXV 1/2",
ifelse(chrom=="utg122_pilon","Spar chrXV 2/2",
ifelse(chrom=="utg675_pilon","Spar chrXVI",
ifelse(chrom=="chrI","Scer chrI",
ifelse(chrom=="chrII","Scer chrII",
ifelse(chrom=="chrIII","Scer chrIII",
ifelse(chrom=="chrIV","Scer chrIV",
ifelse(chrom=="chrV","Scer chrV",
ifelse(chrom=="chrVI","Scer chrVI",
ifelse(chrom=="chrVII","Scer chrVII",
ifelse(chrom=="chrVIII","Scer chrVIII",
ifelse(chrom=="chrIX","Scer chrIX",
ifelse(chrom=="chrX","Scer chrX",
ifelse(chrom=="chrXI","Scer chrXI",
ifelse(chrom=="chrXII","Scer chrXII",
ifelse(chrom=="chrXIII","Scer chrXIII",
ifelse(chrom=="chrXIV","Scer chrXIV",
ifelse(chrom=="chrXV","Scer chrXV",
ifelse(chrom=="chrXVI","Scer chrXVI",NA)))))))))))))))))))))))))))))))))))
all_pools_samples<- all_mean_genome_chrom
all_pools_samples$log2 <- log2(all_pools_samples$mean_window)
all_pools_samples$gainloss <-ifelse(all_pools_samples$log2>7,"Gain",NA)
all_pools_samples$gainloss <-ifelse(all_pools_samples$log2<2,"Loss",all_pools_samples$gainloss)
all_pools_samples<-all_pools_samples %<>% mutate(chrom2 = ifelse(chrom=="utg351_pilon",17,
ifelse(chrom=="utg1271_pilon",18,
ifelse(chrom=="utg584_pilon",19,
ifelse(chrom=="utg988_pilon",20,
ifelse(chrom=="utg69_pilon",21,
ifelse(chrom=="utg639_pilon",22,
ifelse(chrom=="utg199_pilon",23,
ifelse(chrom=="utg176_pilon",24,
ifelse(chrom=="utg1121_pilon",25,
ifelse(chrom=="utg245_pilon",26,
ifelse(chrom=="utg298_pilon",27,
ifelse(chrom=="utg110_pilon",28,
ifelse(chrom=="utg11_pilon",29,
ifelse(chrom=="utg210_pilon",30,
ifelse(chrom=="utg48_pilon",31,
ifelse(chrom=="utg155_pilon",32,
ifelse(chrom=="utg122_pilon",33,
ifelse(chrom=="utg675_pilon",34,
ifelse(chrom=="chrI",1,
ifelse(chrom=="chrII",2,
ifelse(chrom=="chrIII",3,
ifelse(chrom=="chrIV",4,
ifelse(chrom=="chrV",5,
ifelse(chrom=="chrVI",6,
ifelse(chrom=="chrVII",7,
ifelse(chrom=="chrVIII",8,
ifelse(chrom=="chrIX",9,
ifelse(chrom=="chrX",10,
ifelse(chrom=="chrXI",11,
ifelse(chrom=="chrXII",12,
ifelse(chrom=="chrXIII",13,
ifelse(chrom=="chrXIV",14,
ifelse(chrom=="chrXV",15,
ifelse(chrom=="chrXVI",16,NA)))))))))))))))))))))))))))))))))))
all_pools_samples1 <- filter(all_pools_samples, Unique_value=="Yes")
all_pools_samples1$Replicate <- as.numeric(all_pools_samples1$Replicate)
NQ_hyb<-all_pools_samples%>% filter(Type=="Evolved_NQO")
NQ_hyb<-NQ_hyb %>% filter(Strain=="3Hybrid")
NQ_hyb28 <- NQ_hyb %>% filter(Replicate == 28)
hybnq <-NQ_hyb28
FigSupp6B <-hybnq %>%
ggplot(aes(x=interaction(start,as.numeric(chrom2)), y=log2, col = chrom)) +
xlab("Chromosome") +
geom_point(col="#FF9999",size=0.00002) +
theme(plot.title = element_text(hjust = 0.5))+
scale_colour_manual(values=c("grey","grey", "grey",
"grey","grey","grey",
"grey","grey","grey","grey","grey",
"grey","grey","grey","grey","grey",
"grey","grey", "grey",
"grey","grey","grey",
"grey","grey","grey","grey","grey",