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Bautista2024_Figure2.R
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#Main Figures of the Manuscript Bautista_2024
##Figure 2###
#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("flowCore")
library(flowCore)
#install.packages("flowViz")
library(flowViz)
#install.packages("foreign")
library(foreign)
#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("")
#################
####1.Get data####
genomics <- read_csv("2_growth.csv")
norm_aneu_heatmap <- read.csv("2A_data_aneuploidies_heatmap_fitness_gain.csv")
norm_aneu <- read.csv("2A_data_aneuploidies.csv")
B_Table_LOH_Line13<-read.csv("2B_Table_LOH_Line13.csv")
D_Table_LOH_Percentage<-read.csv("2D_Table_LOH_Percentage.csv")
E_Table_counts_LOH<-read.csv("2E_Table_counts_LOH.csv")
#################
######Figure 2A######
#Heatmap read depth
#Define some elements for the graph
cond.labs <- c(
`Evolved_control` = "Evolved in control",
`Evolved_NQO` = "Evolved in UV mimetic",
`Ancestor` = "Ancestor"
)
make_labels <- function(labels) {
result <- str_split(labels, "\\.")
unlist(lapply(result, function(x) x[1]))
}
legend_title <- "Relative Read Depth"
# Generate separate panels for each genotypic backgrounds since Line numbers are ordered by fitness gain in 4-NQO
#HYBRID
g_heatmap<-norm_aneu_heatmap
ghyb <- g_heatmap%>% dplyr:::filter(Strain=="3Hybrid")
ghyb <- ghyb%>% dplyr:::filter(Type=="Evolved_NQO")
g <- ghyb %>% ggplot(aes(y = interaction(Replicate,Strain,fitness_gain_evolved_in_NQ0), x = as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1))
ghybC <- g + scale_fill_gradient2(legend_title,low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))+
scale_x_discrete(breaks=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,
18,19,20,21,22,23,24,25,26,27,28,29,30,31,32),
labels=c("Scer chrI","Spar chrI","Scer chrII","Spar chrII",
"Scer chrIII","Spar chrIII","Scer chrIV","Spar chrIV",
"Scer chrV","Spar chrV","Scer chrVI","Spar chrVI",
"Scer chrVII","Spar chrVII","Scer chrVIII","Spar chrVIII",
"Scer chrIX","Spar chrIX","Scer chrX","Spar chrX",
"Scer chrXI","Spar chrXI","Scer chrXII","Spar chrXII",
"Scer chrXIII","Spar chrXIII","Scer chrXIV","Spar chrXIV",
"Scer chrXV","Spar chrXV",
"Scer chrXVI","Spar chrXVI"))+
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 15),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
xlab("Chromosome")+
theme(axis.text.y=element_text(size=8),
axis.text.x=element_text(size=9))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
theme(legend.title = element_text(angle = 0),#, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
theme(legend.key.size = unit(0.4, 'cm'), #change legend key size
legend.key.height = unit(0.4, 'cm'), #change legend key height
legend.key.width = unit(0.6, 'cm'), #change legend key width
legend.title = element_text(size=8), #change legend title font size
legend.text = element_text(size=7))+
theme(axis.title = element_text(size=15, face = "bold"),
axis.text = element_text(size=11),
strip.text = element_text(face = "bold", size = 14),
strip.background = element_blank(),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top",
legend.direction = "horizontal") #+
geom_tile(aes(fill =Normalized_read),height=0.65,size=0.2)
legheatmap <- get_legend(ghybC)
ghybC <- g + scale_y_discrete(labels = make_labels, name = "Replicate") +
scale_fill_gradient2(legend_title,low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))+
scale_x_discrete(breaks=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,
18,19,20,21,22,23,24,25,26,27,28,29,30,31,32),
labels=c("Scer chrI","Spar chrI","Scer chrII","Spar chrII",
"Scer chrIII","Spar chrIII","Scer chrIV","Spar chrIV",
"Scer chrV","Spar chrV","Scer chrVI","Spar chrVI",
"Scer chrVII","Spar chrVII","Scer chrVIII","Spar chrVIII",
"Scer chrIX","Spar chrIX","Scer chrX","Spar chrX",
"Scer chrXI","Spar chrXI","Scer chrXII","Spar chrXII",
"Scer chrXIII","Spar chrXIII","Scer chrXIV","Spar chrXIV",
"Scer chrXV","Spar chrXV",
"Scer chrXVI","Spar chrXVI"))+
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 15),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+#+theme(strip.text.y = element_blank())
xlab("Chromosome")+
theme(legend.background = element_rect(fill="snow2",
size=0.4, linetype="solid",colour ="grey"))+
theme(axis.text.y=element_text(size=8),
axis.text.x=element_text(size=9))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
theme(legend.title = element_text(angle = 270, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
theme (legend.position="none")
ghybC2 <- ghybC + geom_rect(aes(xmin = 1.5, xmax = 2.5, ymin = 9.5, ymax = 10.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 3.5, xmax = 4.5, ymin = 5.5, ymax = 6.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 27.5, xmax = 28.5, ymin = 3.5, ymax = 4.5),color = "blue4",fill = NA)+
geom_rect(aes(xmin = 23.5, xmax = 24.5, ymin = 3.5, ymax = 4.5),color = "blue4",fill = NA)+
#geom_rect(aes(xmin = 12.5, xmax = 13.5, ymin = 20.5, ymax = 21.5),color = "firebrick4",fill = NA)+
#geom_rect(aes(xmin = 13.5, xmax = 14.5, ymin = 20.5, ymax = 21.5),color = "blue4",fill = NA)+
geom_rect(aes(xmin = 14.5, xmax = 15.5, ymin = 21.5, ymax = 22.5),color = "blue4",fill = NA)
ghyb <- g_heatmap%>% dplyr:::filter(Strain=="3Hybrid")
ghyb <- ghyb%>% dplyr:::filter(Type=="Evolved_control")
g <- ghyb %>% ggplot(aes(y = interaction(Replicate,Strain), as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +ggtitle("Hybrid")
ghybA <- g +
scale_y_discrete(limits=c("14.3Hybrid","21.3Hybrid","24.3Hybrid","4.3Hybrid","26.3Hybrid",
"10.3Hybrid","11.3Hybrid","9.3Hybrid","2.3Hybrid","25.3Hybrid",
"5.3Hybrid","22.3Hybrid","30.3Hybrid","27.3Hybrid","13.3Hybrid",
"16.3Hybrid", "17.3Hybrid","6.3Hybrid","3.3Hybrid","7.3Hybrid",
"19.3Hybrid","1.3Hybrid","23.3Hybrid","29.3Hybrid","28.3Hybrid",
"15.3Hybrid","20.3Hybrid","12.3Hybrid","18.3Hybrid","8.3Hybrid"),
labels=c(14,21,24,4,26,10,11,9,2,25,5,22,30,27,13,
16,17,6,3,7,19,1,23,29,28,15,20,12,18,8)
)+theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 15),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+#+theme(strip.text.y = element_blank())
theme (legend.position="none")+
xlab("Chromosome")+
theme(axis.text.y=element_text(size=8),
axis.text.x=element_blank())+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
scale_fill_gradient2(low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))+
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank())
ghybA2 <- ghybA + geom_rect(aes(xmin = 21.5, xmax = 22.5, ymin = 23.5, ymax = 24.5),color = "firebrick4",fill = NA)
HYBRID <- plot_grid(ghybA2,ghybC2, nrow = 2,rel_heights = c(1,1.15))
#S.PARADOXUS
gspar <- g_heatmap%>% dplyr:::filter(Strain=="2Spar")
gspar <- gspar%>% dplyr:::filter(Type=="Evolved_control")
g <- gspar %>% ggplot(aes(y = interaction(Replicate,Strain,fitness_gain_evolved_in_NQ0), as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +ggtitle("Saccharomyces paradoxus")
gsparA <- g + scale_y_discrete(labels = make_labels, name = "Replicate") +
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 11),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
scale_fill_gradient2(low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))+
theme (legend.position="none") +
theme(strip.text.y = element_blank(),
axis.text.y=element_text(size=8))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold.italic"))
gspar <- g_heatmap%>% dplyr:::filter(Strain=="2Spar")
gspar <- gspar%>% dplyr:::filter(Type=="Evolved_NQO")
g <- gspar %>% ggplot(aes(y = interaction(Replicate,Strain,fitness_gain_evolved_in_NQ0), as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1))
gsparC <- g + scale_y_discrete(labels = make_labels, name = "Replicate") +
scale_x_discrete(breaks=c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32),
labels=c("Spar chrI","Spar chrII",
"Spar chrIII","Spar chrIV",
"Spar chrV","Spar chrVI",
"Spar chrVII","Spar chrVIII",
"Spar chrIX","Spar chrX",
"Spar chrXI","Spar chrXII",
"Spar chrXIII","Spar chrXIV",
"Spar chrXV","Spar chrXVI"))+
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 11),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
theme (legend.position="none")+
xlab("Chromosome")+
theme(strip.text.y = element_blank(),
axis.text.y=element_text(size=8))+
scale_fill_gradient2(low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))
gsparC2<- gsparC+geom_rect(aes(xmin = 12.5, xmax = 13.5, ymin = 3.5, ymax = 4.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 14.5, xmax = 15.5, ymin = 4.5, ymax = 5.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 14.5, xmax = 15.5, ymin = 5.5, ymax = 6.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 14.5, xmax = 15.5, ymin = 7.5, ymax = 8.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 12.5, xmax = 13.5, ymin = 21.5, ymax = 22.5),color = "firebrick4",fill = NA)+
#geom_rect(aes(xmin = 12.5, xmax = 13.5, ymin = 20.5, ymax = 21.5),color = "firebrick4",fill = NA)+
#geom_rect(aes(xmin = 13.5, xmax = 14.5, ymin = 20.5, ymax = 21.5),color = "blue4",fill = NA)+
geom_rect(aes(xmin = 12.5, xmax = 13.5, ymin = 24.5, ymax = 25.5),color = "firebrick4",fill = NA)
SPAR <- plot_grid(gsparA,gsparC2, nrow = 2,rel_heights = c(1,1.15))
#S.CEREVISIAE
gscer <- g_heatmap%>% dplyr:::filter(Strain=="1Scer")
gscer <- gscer%>% dplyr:::filter(Type=="Evolved_control")
g <- gscer %>% ggplot(aes(y = interaction(Replicate,Strain,fitness_gain_evolved_in_NQ0), as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +ggtitle("Saccharomyces cerevisiae")
gscerA <- g +scale_y_discrete(labels = make_labels, name = "Line") +theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(strip.text = element_text(face = "bold", size = 12),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=15,face = "bold"))+#+theme(strip.text.y = element_blank())
scale_fill_gradient2(low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))+
theme (legend.position="none") +
theme(strip.text.y = element_blank())+
ylab("Line")+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold.italic"),
axis.text.y=element_text(size=8))
gscer <- g_heatmap%>% dplyr:::filter(Strain=="1Scer")
gscer <- gscer%>% dplyr:::filter(Type=="Evolved_NQO")
g <- gscer %>% ggplot(aes(y = interaction(Replicate,Strain,fitness_gain_evolved_in_NQ0), as.factor(chrom4))) + facet_grid(Type~., labeller = as_labeller(cond.labs))
g <- g + geom_tile(aes(fill = log2_anc_norm_aneu), color = "white", lwd = .7) +theme(axis.text.x = element_text(angle = 90, hjust = 1))
gscerC <- g + scale_y_discrete(labels = make_labels, name = "Line") +
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold.italic"))+
scale_x_discrete(breaks=c(1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31),
labels=c("Scer chrI","Scer chrII",
"Scer chrIII","Scer chrIV",
"Scer chrV","Scer chrVI",
"Scer chrVII","Scer chrVIII",
"Scer chrIX","Scer chrX",
"Scer chrXI","Scer chrXII",
"Scer chrXIII","Scer chrXIV",
"Scer chrXV","Scer chrXVI"))+
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(strip.text = element_text(face = "bold", size = 16),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.title.y = element_text(size=15,face = "bold"))+#+theme(strip.text.y = element_blank())
theme (legend.position="none")+
xlab("Line")+
xlab("Chromosome")+
theme(strip.text.y = element_blank(),
axis.text.y=element_text(size=8),
axis.text.x=element_text(size=9),
axis.title = element_text(size = 15,face="bold"))+
xlab("Line")+
scale_fill_gradient2(low = "darkslateblue", mid = "gray90", high = "#cc7000", midpoint = 0,limits= c(-2.9,2.9),breaks= c(-2,-1,0,1,2))
gscerC2<-gscerC+geom_rect(aes(xmin = 14.5, xmax = 15.5, ymin = 0.5, ymax = 1.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 1.5, xmax = 2.5, ymin = 4.5, ymax = 5.5),color = "firebrick4",fill = NA)+
geom_rect(aes(xmin = 0.5, xmax = 1.5, ymin = 25.5, ymax = 26.5),color = "firebrick4",fill = NA)
SCER <- plot_grid(gscerA,gscerC2, nrow = 2,rel_heights = c(1,1.15))
#create new legend
data <- data.frame(
x = c(1, 3),
y = c("Chromosome gain","Chromosome loss"))
# Create the plot
leg2 <-ggplot(data, aes(x = x, y = y,col=as.factor(y))) +
geom_point(shape=22,size=6)+
theme(legend.direction ="horizontal")+
scale_colour_manual(values=c("firebrick4","blue4"))+
geom_rect(xmin = 0.5, xmax = 1.5, ymin = 4.5, ymax = 5.5,, color = "firebrick4", fill = "white") +
geom_rect(xmin = 1.5, xmax = 2.5, ymin = 4.5, ymax = 5.5,, color = "blue4", fill = "white") +
theme_bw()+
theme(axis.text = element_text(face="bold"))+
theme(axis.title = element_blank(),
strip.text = element_text(face = "bold", size = 15),
strip.background = element_blank()) +
theme(axis.title.x=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
xlab("Chromosome")+
theme(axis.text.y=element_text(size=8),
axis.text.x=element_text(size=9))+
theme(plot.title = element_text(hjust = 0.5, size=12, face = "bold"))+
theme(legend.title = element_text(angle = 0),#, vjust= 0.2,hjust= 0.9),
legend.title.align=3)+
theme(legend.key.size = unit(0.4, 'cm'), #change legend key size
legend.key.height = unit(0.4, 'cm'), #change legend key height
legend.key.width = unit(0.6, 'cm'), #change legend key width
legend.title = element_text(size=8), #change legend title font size
legend.text = element_text(size=7))+
theme(axis.title = element_text(size=15, face = "bold"),
axis.text = element_text(size=11),
strip.text = element_text(face = "bold", size = 14),
strip.background = element_blank(),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top",
legend.direction = "horizontal")+
theme(legend.title=element_blank())
legheatmap2 <- get_legend(leg2)
# Combine the panels to create a merged plot
Fig2Afitness <- plot_grid(SCER,SPAR,HYBRID, nrow = 1,rel_widths = c(4,4,8))
Fig2A<- Fig2Afitness
legheatmap3<-plot_grid(legheatmap,legheatmap2)
Fig2Afitness2 <- plot_grid(legheatmap3,Fig2Afitness, nrow=2,rel_heights = c(0.4,5))
Fig2A<- Fig2Afitness2
#################
######Figure 2B######
###Example t-LOH on line 13
legend_title <- "Relative Read Depth"
Fig2Bb <-B_Table_LOH_Line13%>% ggplot(aes(x = as.numeric(start2),y = as.factor(chrom4),fill=Normalized_read)) +
theme_prism()+
theme_bw(base_size=24) +
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))+
facet_wrap(.~ Replicate, ncol=6)+
scale_y_discrete(breaks=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32),
labels=c("Scer chrI","Spar chrI",
"Scer chrII","Spar chrII",
"Scer chrII","Spar chrIII",
"Scer chrIV","Spar chrIV",
"Scer chrV","Spar chrV",
"Scer chrVI","Spar chrVI",
"Scer chrVII","Spar chrVII",
"Scer chrVIII","Spar chrVIII",
"Scer chrIX","Spar chrIX",
"Scer chrX","Spar chrX",
"Scer chrXI","Spar chrXI",
"Scer chrXII","Spar chrXII",
"Scer chrXIII","Spar chrXIII",
"Scer chrXIV","Spar chrXIV",
"Scer chrXV","Spar chrXV",
"Scer chrXVI","Spar chrXVI"),
limits=rev)+
scale_x_continuous(breaks=c(0,500000,1000000,1500000),
labels=c(0,500,1000,1500))+
xlab("Coordinate (kbp)")+
ylab("Chromosome")+
theme(legend.key.size = unit(0.4, 'cm'),
legend.key.height = unit(0.4, 'cm'),
legend.key.width = unit(0.6, 'cm'),
axis.title = element_text(size=15, face = "bold"),
axis.text = element_text(size=11),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top",
legend.direction = "horizontal",
legend.margin = margin(t = -0.2, r = -0.2, b = -0.2, l = -0.2),
panel.spacing = margin(0, 0, 0, 0),
strip.text = element_blank(),
strip.background = element_blank())+
theme(legend.position="none")
Fig2B <- plot_grid(legheatmap,Fig2Bb, nrow=2,rel_heights = c(0.29,5))
#################
######Figure 2C######
###t-LOH scheme
img <-image_read_pdf("Fig2C.pdf")
gpp <- rasterGrob(img, interpolate = TRUE)
Fig2C <- plot_grid(gpp)
#################
######Figure 2D######
#Percentage of LOH across genomes
legend_title <- "% of hybrid lines bearing \n LOH in UV mimetic"
Fig2D <- D_Table_LOH_Percentage %>% dplyr:::filter(genome_parental=="Spar") %>% ggplot(aes(x = as.numeric(start2),y = as.factor(chrom4),fill=Percentage)) +
geom_tile(aes(fill =Percentage,height=0.65)) +
scale_fill_gradient2(legend_title,low = "wheat", mid="indianred4",high="red3",midpoint = 50, limits = c(0, 100))+
scale_y_discrete(breaks=c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32),
labels=c("chrI","chrII",
"chrIII","chrIV",
"chrV","chrVI",
"chrVII","chrVIII",
"chrIX","chrX",
"chrXI","chrXII",
"chrXIII","chrXIV",
"chrXV","chrXVI"),
limits=rev)+
scale_x_continuous(breaks=c(0,500000,1000000,1500000),
labels=c(0,500,1000,1500))+
xlab("Coordinate (kbp)")+
ylab("Chromosome")+
theme_bw(base_size=24) +
theme(legend.key.size = unit(0.4, 'cm'),
legend.key.height = unit(0.4, 'cm'),
legend.key.width = unit(0.6, 'cm'),
axis.title = element_text(size=15, face = "bold"),
axis.text = element_text(size=11),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top",
legend.direction = "horizontal",
legend.margin = margin(t = 0, r = 0, b = 0, l = 0),
strip.text = element_blank(),
strip.background = element_blank())+
theme(legend.spacing.x = unit(0.1, 'cm'),
legend.text = element_text(margin = margin(t = 5)))
leg_percentage <- get_legend(Fig2D)
Fig2D <- D_Table_LOH_Percentage %>% dplyr:::filter(genome_parental=="Spar") %>% ggplot(aes(x = as.numeric(start2),y = as.factor(chrom4),fill=Percentage)) +
geom_tile(aes(fill =Percentage,height=0.65)) +
scale_fill_gradient2(legend_title,low = "wheat", mid="indianred4",high="red3",midpoint = 50, limits = c(0, 100))+
scale_y_discrete(breaks=c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32),
labels=c("chrI","chrII",
"chrIII","chrIV",
"chrV","chrVI",
"chrVII","chrVIII",
"chrIX","chrX",
"chrXI","chrXII",
"chrXIII","chrXIV",
"chrXV","chrXVI"),
limits=rev)+
scale_x_continuous(breaks=c(0,500000,1000000,1500000),
labels=c(0,500,1000,1500))+
xlab("Coordinate (kbp)")+
ylab("Chromosome")+
theme_bw(base_size=24) +
theme(legend.key.size = unit(0.4, 'cm'),
legend.key.height = unit(0.4, 'cm'),
legend.key.width = unit(0.6, 'cm'),
axis.title = element_text(size=15, face = "bold"),
axis.text = element_text(size=11),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.position = "top",
legend.direction = "horizontal",
legend.margin = margin(t = 0, r = 0, b = 0, l = 0),
strip.text = element_blank(),
strip.background = element_blank())+
theme(legend.position = "none")
Fig2Dleg <- plot_grid(leg_percentage,Fig2D, nrow=2,rel_heights = c(0.45,5))
#################
######Figure 2E######
#Boxplot LOH control vs UV mimetic conditions
#Exclude triploids
control<-E_Table_counts_LOH %>% filter(Type=="Evolved_control")
control <- control %>% filter (Replicate!=20)
control <- control %>% filter (Replicate!=23)
control <- control %>% filter (Replicate!=26)
control <- control %>% filter (Replicate!=27)
control <- control %>% filter (Replicate!=28)
nqo<-E_Table_counts_LOH %>% filter(Type=="Evolved_NQO")
nqo <- nqo %>% filter (Replicate!=26)
nqo <- nqo %>% filter (Replicate!=27)
nqo <- nqo %>% filter (Replicate!=28)
E_Table_counts_LOH<- rbind(nqo, control)
wilcox.test(nqo$new_counts, control$new_counts)
Fig2E <- ggplot(E_Table_counts_LOH, aes(x = Type, y = new_counts))+
theme_prism() +
geom_violin(colour="black",pch=21,size=1,alpha=0.5, fill="#FF9999",trim=TRUE)+#width=1.15)+
theme(legend.position = "bottom",
legend.direction = "horizontal")+
geom_segment(aes(x=1, xend=2, y=3.2, yend=3.2)) +
annotate("text",
y = c(3.4),
x = c(1.5),
label = c("p < 0.0001"),
family = "", fontface = 3, size=4) +
ylab("Number of \n t-LOH / line")+
#xlab(" ")+
theme_bw(base_size=24) +
theme(axis.title = element_text(size=8, face = "bold"),
strip.text = element_text(face = "bold", size = 9),
strip.background = element_blank(),
axis.text.x = element_text(size=7),
axis.text.y = element_text(size=8),
panel.background = element_blank()) +theme(strip.text.x = element_blank())+
theme(plot.title = element_text(size=15, hjust=0, face = "bold", color="black"),
axis.text.x = element_text(size = 13, color="black",face = "bold"),
axis.text.y = element_text(size = 13, color="black"),
axis.title = element_text(size = 15,face="bold"),
axis.title.x=element_blank())+
scale_x_discrete("", labels=c("Evolved in \n control"," Evolved in \n UV mimetic"))
#################
############Assemble and save Figure 2############
Fig2A2<- plot_grid(Fig2A,labels="a",label_size=20)
Fig2B2<- plot_grid(Fig2B,labels="b",label_size=20)
Fig2C2<- plot_grid(Fig2C,labels="c",label_size=20)
Fig2D2<- plot_grid(Fig2Dleg,labels="d",label_size=20)
Fig2E2<- plot_grid(Fig2E,labels="e",label_size=20)
Fig2DE <- plot_grid(Fig2D2,Fig2E2,nrow=2,rel_heights =c(2.5,1.4))
Fig2BC <- plot_grid(Fig2B2,Fig2C2,nrow=1)
Fig2BBCDE<- plot_grid(Fig2BC,Fig2DE,nrow=1, rel_widths =c(2,1))
Fig2<- plot_grid(Fig2A2,Fig2BBCDE,nrow=2, rel_heights =c(2,2))
#Save the image in the previously set working directory
ggsave (plot = Fig2, filename = "Bautista2024_Figure2_low_quality.jpg", units = "cm", device = "jpg",width =33, height =35, dpi = 300,bg = "white")
ggsave (plot = Fig2, filename = "Bautista2024_Figure2.png", units = "cm", device = "png",width =33, height =35, dpi = 1000,bg = "white")
ggsave (plot = Fig2, filename = "Bautista2024_Figure2.jpg", units = "cm", device = "jpg",width =33, height =35, dpi = 1000,bg = "white")
ggsave (plot = Fig2, filename = "Bautista2024_Figure2.svg", units = "cm", device = "svg",width =33, height =35, dpi = 1000,bg = "white")
ggsave (plot = Fig2, filename = "Bautista2024_Figure2.pdf", units = "cm", device = "pdf",width =33, height =35, dpi = 1000,bg = "white")
#################