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cross-study_BMC_HRD.R
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cross-study_BMC_HRD.R
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library(plyr)
library(dplyr)
library(tidyr)
library(reshape2)
library(ggplot2)
library(tidyverse)
library(ggpubr)
library(zoo)
library(scales)
library(data.table)
library(ggrepel)
library(forcats)
library(readxl)
library(scales)
library(ggridges)
library(survival)
library(wesanderson)
library(viridis)
library(pals)
BRCA<-c("BRCA1", "BRCA2")
HRR_13<-c( "ATM", "BRIP1", "PALB2", "RAD51C", "BARD1", "CDK12", "CHEK1", "CHEK2", "FANCL", "PPP2R2A", "RAD51B", "RAD51D", "RAD54L")
HRR_myr<-c("BRCA1", "BRCA2", "ATM", "BRIP1", "PALB2", "RAD51C", "BARD1", "CDK12", "CHEK1", "CHEK2", "FANCL", "PPP2R2A", "RAD51B", "RAD51D", "RAD54L")
###read in all files:
PAOLA<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/PAOLA/PAOLA_summary_paper_withfails.csv")
OPINION<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/OPINION/OPINION_paper_refined.csv")
LIGHT<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/LIGHT/LIGHT_paper_refined.csv")
Study19<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Study19/AZO_HRD_HRR_results_summary_Ecode_added_nonHRR_GS_added.csv")
SOLO1<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/SOLO1/SOLO1_Myriad_data_summary_paper.csv")
SOLO2<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/SOLO2/SOLO2_Myriad_data_summary_20200624.csv")
####remove test fails:
OPINION<-OPINION[!OPINION$Status =="Test Failed/Cancelled",]
LIGHT<-LIGHT[!LIGHT$Status =="Test Failed/Cancelled",]
#HRD fails
length(PAOLA$SUBJECT[PAOLA$HRD_Score =="FAILED"])
length(PAOLA$SUBJECT[PAOLA$HRD_Score =="FAILED"])/length(PAOLA$SUBJECT)*100
PAOLA_fail<-c(length(PAOLA$SUBJECT[PAOLA$HRD_Score =="FAILED"]),length(PAOLA$SUBJECT[PAOLA$HRD_Score =="FAILED"])/length(PAOLA$SUBJECT)*100)
length(OPINION$SUBJECT[OPINION$HRD_Score =="FAILED"])
length(OPINION$SUBJECT[OPINION$HRD_Score =="FAILED"])/length(OPINION$SUBJECT)
OPINION_fail<-c(length(OPINION$SUBJECT[OPINION$HRD_Score =="FAILED"]),length(OPINION$SUBJECT[OPINION$HRD_Score =="FAILED"])/length(OPINION$SUBJECT)*100)
length(LIGHT$SUBJECT[LIGHT$HRD_Score =="FAILED"])
length(LIGHT$SUBJECT[LIGHT$HRD_Score =="FAILED"])/length(LIGHT$SUBJECT)*100
LIGHT_fail<-c(length(LIGHT$SUBJECT[LIGHT$HRD_Score =="FAILED"]),length(LIGHT$SUBJECT[LIGHT$HRD_Score =="FAILED"])/length(LIGHT$SUBJECT)*100)
length(Study19$SUBJECT[Study19$HRD_Score =="FAILED"])
length(Study19$SUBJECT[Study19$HRD_Score =="FAILED"])/length(Study19$SUBJECT)*100
Study19_fail<-c(length(Study19$SUBJECT[Study19$HRD_Score =="FAILED"]),length(Study19$SUBJECT[Study19$HRD_Score =="FAILED"])/length(Study19$SUBJECT)*100)
length(SOLO1$SUBJECT[SOLO1$HRD_Score =="FAILED"])
length(SOLO1$SUBJECT[SOLO1$HRD_Score =="FAILED"])/length(SOLO1$SUBJECT)*100
SOLO1_fail<-c(length(SOLO1$SUBJECT[SOLO1$HRD_Score =="FAILED"]),length(SOLO1$SUBJECT[SOLO1$HRD_Score =="FAILED"])/length(SOLO1$SUBJECT)*100)
length(SOLO2$SUBJECT[SOLO2$HRD_Score =="FAILED"])
length(SOLO2$SUBJECT[SOLO2$HRD_Score =="FAILED"])/length(SOLO2$SUBJECT)*100
SOLO2_fail<-c(length(SOLO2$SUBJECT[SOLO2$HRD_Score =="FAILED"]),length(SOLO2$SUBJECT[SOLO2$HRD_Score =="FAILED"])/length(SOLO2$SUBJECT)*100)
all_HRD_fail<-rbind(PAOLA_fail,OPINION_fail,LIGHT_fail,Study19_fail,SOLO1_fail,SOLO2_fail)
all_HRD<-rbind(PAOLA,OPINION,LIGHT,Study19,SOLO1,SOLO2)
length(all_HRD$SUBJECT[all_HRD$HRD_Score =="FAILED"])
length(all_HRD$SUBJECT[all_HRD$HRD_Score !="FAILED"])
length(all_HRD$SUBJECT[all_HRD$HRD_Score =="FAILED"])/length(all_HRD$SUBJECT)*100
all_HRD_fail_reason<-data.frame(table(all_HRD$Fail_Reason[all_HRD$HRD_Score =="FAILED"]))
all_HRD_fail_reason$Percent <-(all_HRD_fail_reason$Freq/sum(all_HRD_fail_reason$Freq))*100
write.csv(all_HRD,file="/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20200619.csv",row.names=FALSE)
all_HRD<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20200619.csv")
all_HRD$Population<-ifelse(all_HRD$Study =="PAOLA" | all_HRD$Study =="SOLO1", "CR/PR after 1L Platinum Based Chemotherapy", "PSR")
all_HRD$Variant_Subtype<-gsub("frameshift/indel","Frameshift/Indel",all_HRD$Variant_Subtype)
all_HRD$Variant_Subtype<-gsub("Loss/Rearrangment","Loss/Rearrangement",all_HRD$Variant_Subtype)
write.csv(all_HRD,file="/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20200629.csv",row.names=FALSE)
all_HRD<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20200629.csv")
all_HRD$Status_3<-ifelse(all_HRD$Status =="tBRCAm", "tBRCAm", "Non-tBRCAm")
all_HRD$HRD_status<-ifelse(all_HRD$Status =="tBRCAm","HRD positive (tBRCAm)",
ifelse(all_HRD$HRD_Score >=42 & all_HRD$Status == "HRRm and non-BRCAm","HRD positive (HRRm)",
ifelse(all_HRD$HRD_Score >=42,"HRD positive (non-HRRm)",
ifelse(all_HRD$HRD_Score < 42 & all_HRD$Status == "HRRm and non-BRCAm", "HRD negative (HRRm)",
ifelse(all_HRD$HRD_Score < 42, "HRD negative (non-HRRm)",
ifelse(all_HRD$HRD_Score =="FAILED" & all_HRD$Status == "HRRm and non-BRCAm", "HRD unknown (HRRm)","HRD unknown"))))))
write.csv(all_HRD,file="/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20201020_int.csv",row.names=FALSE)
all_HRD<-read.csv("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/all_HRD_20201020_int.csv")
all_HRD_plots<-all_HRD[!all_HRD$HRD_Score =="FAILED",]
all_HRD_plots<-all_HRD_plots[!is.na(all_HRD_plots$Status),]
#all_HRD_plots<-all_HRD_plots[!is.na(all_HRD$HRD_Score),]
all_HRD_plots$HRD_Score <-as.numeric(as.character(all_HRD_plots$HRD_Score))
all_HRD_plots$Status <- gsub("HRRm and non-BRCAm","Non-BRCA HRRm", all_HRD_plots$Status)
all_HRD_plots$Status <- factor(all_HRD_plots$Status, levels=c( "tBRCAm","Non-BRCA HRRm","Non-HRRm")) #reordering zygosity variables to appear in this order in the barplot
all_HRD_plots$Status_3 <- factor(all_HRD_plots$Status_3, levels=c( "tBRCAm","Non-tBRCAm")) #reordering zygosity variables to appear in this order in the barplot
all_HRD_plots$Study <- factor(all_HRD_plots$Study, levels=c( "PAOLA","OPINION","LIGHT","Study 19", "SOLO1", "SOLO2")) #reordering zygosity variables to appear in this order in the barplot
all_HRD_plots$key<-paste(all_HRD_plots$SUBJECT,all_HRD_plots$Study,sep="_")
t.test(all_HRD_plots$HRD_Score[all_HRD_plots$Status =="tBRCAm"],all_HRD_plots$HRD_Score[all_HRD_plots$Status =="Non-HRRm"])
t.test(all_HRD_plots$HRD_Score[all_HRD_plots$Status =="tBRCAm"],all_HRD_plots$HRD_Score[all_HRD_plots$Status =="Non-BRCA HRRm"])
all_HRD_plots<-all_HRD_plots[!duplicated(all_HRD_plots$key),]
###Proportion of tBRCAm tumours >42
#all_HRD$HRD_Score <-as.numeric(as.character(all_HRD$HRD_Score))
dim(all_HRD[all_HRD$Status =="tBRCAm",])
dim(all_HRD[all_HRD$Status =="tBRCAm",])/dim(all_HRD)
dim(all_HRD[all_HRD$Status !="tBRCAm",])
dim(all_HRD[all_HRD$Status !="tBRCAm",])/dim(all_HRD)
dim(all_HRD[all_HRD$Status =="tBRCAm" & all_HRD$HRD_Score != "FAILED",])
dim(all_HRD[all_HRD$Status =="tBRCAm" & all_HRD$HRD_Score != "FAILED",])/dim(all_HRD[all_HRD$Status =="tBRCAm",])
dim(all_HRD[all_HRD$Status =="tBRCAm" & all_HRD$HRD_Score != "FAILED" & as.numeric(as.character(all_HRD$HRD_Score)) >= 42,])
dim(all_HRD[all_HRD$Status =="tBRCAm" & all_HRD$HRD_Score != "FAILED" & as.numeric(as.character(all_HRD$HRD_Score)) >= 33,])
#####histogram with density plots:
tiff("/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/Figures/Plot1_density_hist_all_studies.tiff", units="in", width=5, height=5, res=300)
ggplot(all_HRD_plots, aes(x=HRD_Score))+
geom_histogram(aes(y = ..density..),color="blue",fill="white",binwidth=2)+
geom_density(alpha=.2,fill="#FF6655")+
xlab("HRD Score")+
ylab("Normalised Density")+
theme_bw()+
scale_x_continuous(limits = c(-1, 101), breaks = seq(0, 100, by = 25))+
geom_vline(xintercept=42,linetype="dashed")+
theme(axis.text=element_text(size=8),axis.title=element_text(size=8,face="bold"))+
theme(legend.title=element_text(size=8,face="bold"))+
theme(legend.text=element_text(size=8))+
theme(legend.position = 'top')
dev.off()
####density plots: ridges
all_HRD_plots$Status_Ridge<-case_when(all_HRD_plots$Status =="tBRCAm" ~"tBRCAm (n=863)",
all_HRD_plots$Status =="Non-BRCA HRRm" ~"Non-BRCA HRRm (n=107)",
all_HRD_plots$Status =="Non-HRRm" ~"Non-HRRm (n=868)")
all_HRD_plots$Status_Ridge <- factor(all_HRD_plots$Status_Ridge, levels=c( "tBRCAm (n=863)","Non-BRCA HRRm (n=107)","Non-HRRm (n=868)")) #reordering zygosity variables to appear in this order in the barplot
ggplot(all_HRD_plots,aes(y=Status_Ridge))+
geom_density_ridges(scale= 4,
aes(x = HRD_Score, fill =Status_Ridge),
alpha = .7, color = NA, from = 0, to=100) +
labs(
x = "GIS",
y = "",
title = "",
subtitle = "",
caption = ""
)+
#facet_wrap(~Study,scales="free_x",ncol=1) +
scale_y_discrete(expand = expand_scale(mult = c(0.01, 1.8)))+
#scale_y_discrete(expand = c(0.1, -0.1))+
scale_x_continuous(expand = c(-1, 107))+
geom_vline(xintercept=42,linetype="dashed")+
scale_fill_cyclical(
breaks = c("tBRCAm (n=863)","Non-BRCA HRRm (n=107)","Non-HRRm (n=868)"),
#labels = c(PAOLA = "PAOLA", SCR = "Screening"),
values = c("dodgerblue2","orange","forestgreen"),
name = "Status",
guide = "legend"
) +
theme_ridges(grid = TRUE)+
theme_bw()+
theme(panel.spacing = unit(2, "lines"))+
guides(fill=guide_legend(title=""))+
theme(strip.text.x = element_text(size=20, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=2, linetype="solid"))+
theme(axis.text=element_text(size=25),axis.title=element_text(size=25))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
theme(legend.position = 'top')
ggplot(all_HRD_plots,aes(y=Status))+
geom_density_ridges(scale= 4,
aes(x = HRD_Score, fill =Status),
alpha = .8, color = "black", from = 0, to=100) +
labs(
x = "HRD Score",
y = "",
title = "",
subtitle = "",
caption = ""
)+
facet_wrap(~Study,scales="free_x",ncol=1) +
scale_y_discrete(expand = expand_scale(mult = c(0.01, 1.8)))+
#scale_y_discrete(expand = c(0.1, -0.1))+
scale_x_continuous(expand = c(-1, 104))+
scale_fill_cyclical(
breaks = c("tBRCAm","HRRm and non-BRCAm","Non-HRRm"),
#labels = c(PAOLA = "PAOLA", SCR = "Screening"),
values = c("dodgerblue2","orange","forestgreen"),
name = "Status",
guide = "legend"
) +
theme_ridges(grid = TRUE)+
theme_bw()+
theme(panel.spacing = unit(2, "lines"))+
guides(fill=guide_legend(title=""))+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=2, linetype="solid"))+
theme(axis.text=element_text(size=16),axis.title=element_text(size=16,face="bold"))+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
all_HRD_plots_BRCA<-subset(all_HRD_plots,all_HRD_plots$Status_3 =="tBRCAm")
all_HRD_plots_BRCA_LOH<-all_HRD_plots_BRCA[!all_HRD_plots_BRCA$LOH =="Unknown",]
all_HRD_plots_BRCA_LOH<-all_HRD_plots_BRCA_LOH[!is.na(all_HRD_plots_BRCA_LOH$LOH),]
t.test(all_HRD_plots_BRCA_LOH$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Biallelic Inactivation"],all_HRD_plots$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Heterozygous"])
t.test(all_HRD_plots_BRCA_LOH$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Biallelic Inactivation" & all_HRD_plots_BRCA_LOH$Status_2 =="BRCA1m"],all_HRD_plots$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Heterozygous" & all_HRD_plots_BRCA_LOH$Status_2 =="BRCA1m"])
t.test(all_HRD_plots_BRCA_LOH$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Biallelic Inactivation" & all_HRD_plots_BRCA_LOH$Status_2 =="BRCA2m"],all_HRD_plots$HRD_Score[all_HRD_plots_BRCA_LOH$LOH =="Heterozygous" & all_HRD_plots_BRCA_LOH$Status_2 =="BRCA2m"])
ggplot(all_HRD_plots_BRCA_LOH, aes(x = LOH, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=LOH),outlier.shape = NA)+
xlab("LOH status")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
facet_grid(.~ Status_2,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("Biallelic Inactivation"="dodgerblue","Heterozygous" ="grey49"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("GIS")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
all_HRD_plots_BRCA_LOH1<-all_HRD_plots_BRCA_LOH[!all_HRD_plots_BRCA_LOH$S_G_Status =="s/g BRCA not determined",]
ggplot(all_HRD_plots_BRCA_LOH1, aes(x = LOH, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=LOH),outlier.shape = NA)+
xlab("LOH status")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
facet_grid(.~ S_G_Status,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("Biallelic Inactivation"="dodgerblue","Heterozygous" ="grey49"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("GIS")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
SOLOs<-c("SOLO1","SOLO2")
all_HRD_nsolo<-all_HRD[!all_HRD$Study %in% SOLOs,]
take_out<-c("BRCA1m ; BRCA2m","Test Failed/Cancelled","BRCA1m;tBRCA2m")
all_HRD_plots_1<-all_HRD_plots[!all_HRD_plots$Status_2 %in% take_out,]
####boxplots
#####all
####tBRCAm vs nontBRCAm
ggplot(all_HRD_plots, aes(x = Status_3, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_3),outlier.shape = NA)+
xlab("Subgroup")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("tBRCAm"="dodgerblue2","Non-tBRCAm" ="limegreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
t.test(all_HRD_plots$HRD_Score[all_HRD_plots$Status_3 =="tBRCAm"],all_HRD_plots$HRD_Score[all_HRD_plots$Status_3 =="Non-tBRCAm"])
ggplot(all_HRD_plots, aes(x = Status_3, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_3),outlier.shape = NA)+
xlab("Subgroup")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("Biallelic Inactivation"="dodgerblue2","Heterozygous" ="grey49"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
ggplot(all_HRD_plots_1, aes(x = Status_2, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_2),outlier.shape = NA)+
xlab("Subgroup")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("BRCA1m"="firebrick1","BRCA2m"="deepskyblue2",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(legend.position = "top")+
theme(axis.text=element_text(size=14),axis.title=element_text(size=14,face="bold"))+
theme(legend.title=element_text(size=14,face="bold"))+
theme(legend.text=element_text(size=14))+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
all_HRD_plots_1$Status_4<-case_when(all_HRD_plots_1$Status_2 =="BRCA1m"~"BRCA1m",
all_HRD_plots_1$Status_2 =="BRCA2m"~"BRCA2m",
all_HRD_plots_1$Status_3 =="Non-tBRCAm"~"Non-tBRCAm")
ggplot(all_HRD_plots_1, aes(x = Status_4, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_4),outlier.shape = NA)+
xlab("Subgroup")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")+
facet_grid(.~ Population,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("BRCA1m"="firebrick1","BRCA2m"="deepskyblue2","Non-tBRCAm" ="limegreen"))+
theme_bw()+
theme(legend.position = "top")+
theme(axis.text=element_text(size=14),axis.title=element_text(size=14,face="bold"))+
theme(legend.title=element_text(size=14,face="bold"))+
theme(legend.text=element_text(size=14))+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text=element_text(size=20),axis.title=element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="Non-tBRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="BRCA1m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="Non-tBRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="BRCA2m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="BRCA1m"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_4 =="BRCA2m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status =="HRRm and non-BRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status =="tBRCAm"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status =="HRRm and non-BRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status =="Non-HRRm"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="HRRm and non-BRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="HRRm and non-BRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="HRRm and non-BRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="Non-HRRm"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Population =="CR/PR after 1L Platinum Based Chemotherapy"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Population =="CR/PR after 1L Platinum Based Chemotherapy"])
##### 6 studies
ggplot(all_HRD_plots_1, aes(x = Status_2, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_2),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
facet_grid(.~ Study,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("BRCA1m"="firebrick1","BRCA2m"="deepskyblue2",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=20, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=8),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=20),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="PAOLA"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="PAOLA"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="OPINION"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="OPINION"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="LIGHT"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="LIGHT"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="Study 19"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="Study 19"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="SOLO1"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="SOLO1"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Study =="SOLO2"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Study =="SOLO2"])
##### Population
ggplot(all_HRD_plots_1, aes(x = Status_2, y = HRD_Score),color="black")+
geom_boxplot(aes(fill=Status_2),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
facet_grid(.~ Population,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("BRCA1m"="firebrick1","BRCA2m"="deepskyblue2",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=23, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=20),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=20),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Population =="CR/PR after 1L Platinum Based Chemotherapy"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA1m" & all_HRD_plots_1$Population =="PSR"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Population =="CR/PR after 1L Platinum Based Chemotherapy"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="BRCA2m" & all_HRD_plots_1$Population =="PSR"])
t.test(all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="HRRm and non-BRCAm" & all_HRD_plots_1$Population =="CR/PR after 1L Platinum Based Chemotherapy"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$Status_2 =="HRRm and non-BRCAm" & all_HRD_plots_1$Population =="PSR"])
###G/S BRCAm
all_HRD_plots_2<-all_HRD_plots_1[!is.na(all_HRD_plots_1$S_G_Status),]
all_HRD_plots_2$S_G_Status <- factor(all_HRD_plots_2$S_G_Status, levels=c( "gBRCAm","sBRCAm","s/g BRCA not determined","HRRm and non-BRCAm","Non-HRRm")) #reordering zygosity variables to appear in this order in the barplot
ggplot(all_HRD_plots_2, aes(x = S_G_Status , y = HRD_Score),color="black")+
geom_boxplot(aes(fill=S_G_Status),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Population,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("gBRCAm"="blueviolet","sBRCAm"="cyan3",`s/g BRCA not determined`= "grey50",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=20),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=20),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=20,face="bold"))+
theme(legend.text=element_text(size=20))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
t.test(all_HRD_plots_2$HRD_Score[all_HRD_plots_2$S_G_Status =="gBRCAm"],all_HRD_plots_1$HRD_Score[all_HRD_plots_1$S_G_Status =="sBRCAm"])
###G/S BRCAm
all_HRD_plots_3<-all_HRD_plots_2[!is.na(all_HRD_plots_2$S_G_Status_BRCA1_2),]
all_HRD_plots_3$S_G_Status_BRCA1_2 <- factor(all_HRD_plots_3$S_G_Status_BRCA1_2, levels=c( "gBRCA1m","gBRCA2m","sBRCA1m","sBRCA2m","s/g BRCA not determined BRCA1m","s/g BRCA not determined BRCA2m","HRRm and non-BRCAm","Non-HRRm")) #reordering zygosity variables to appear in this order in the barplot
all_HRD_plots_3$S_G_Status_BRCA1_3<-case_when(all_HRD_plots_3$S_G_Status_BRCA1_2 =="gBRCA1m" ~ "gBRCA1m",
all_HRD_plots_3$S_G_Status_BRCA1_2 =="gBRCA2m" ~ "gBRCA2m",
all_HRD_plots_3$S_G_Status_BRCA1_2 =="sBRCA1m" ~ "sBRCA1m",
all_HRD_plots_3$S_G_Status_BRCA1_2 =="sBRCA2m" ~ "sBRCA2m",
all_HRD_plots_3$S_G_Status_BRCA1_2 =="s/g BRCA not determined BRCA1m" ~ "s/g BRCA not determined BRCA1m",
all_HRD_plots_3$S_G_Status_BRCA1_2 =="s/g BRCA not determined BRCA2m" ~ "s/g BRCA not determined BRCA2m",
all_HRD_plots_3$Status_4 =="Non-tBRCAm" ~ "Non-tBRCAm")
ggplot(all_HRD_plots_3, aes(x = S_G_Status_BRCA1_2 , y = HRD_Score),color="black")+
geom_boxplot(aes(fill=S_G_Status_BRCA1_2),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("gBRCA1m"="blueviolet","gBRCA2m"="blueviolet","sBRCA1m"="cyan3","sBRCA2m"="cyan3",`s/g BRCA not determined BRCA1m`= "grey50",`s/g BRCA not determined BRCA2m`= "grey50",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=12, face= "bold"),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=16),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=16))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
all_HRD_plots_3$S_G_Status_BRCA1_3 <- factor(all_HRD_plots_3$S_G_Status_BRCA1_3, levels=c( "gBRCA1m","gBRCA2m","sBRCA1m","sBRCA2m","s/g BRCA not determined BRCA1m","s/g BRCA not determined BRCA2m","Non-tBRCAm")) #reordering zygosity variables to appear in this order in the barplot
ggplot(all_HRD_plots_3, aes(x = S_G_Status_BRCA1_3 , y = HRD_Score),color="black")+
geom_boxplot(aes(fill=S_G_Status_BRCA1_3),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Study,scales="free")
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("gBRCA1m"="blueviolet","gBRCA2m"="blueviolet","sBRCA1m"="cyan3","sBRCA2m"="cyan3","s/g BRCA not determined BRCA1m"= "grey50","s/g BRCA not determined BRCA2m"= "grey50","Non-tBRCAm" ="limegreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=12, face= "bold"),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=16),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=16))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
all_HRD_plots_3_BRCA<-all_HRD_plots_3[all_HRD_plots_3$Status =="tBRCAm",]
all_HRD_plots_3_BRCA<-all_HRD_plots_3_BRCA[!all_HRD_plots_3_BRCA$S_G_Status =="s/g BRCA not determined",]
all_HRD_plots_3_BRCA<-all_HRD_plots_3_BRCA[!all_HRD_plots_3_BRCA$LOH =="Unknown",]
ggplot(all_HRD_plots_3_BRCA, aes(x = paste(S_G_Status_BRCA1_2,LOH) , y = HRD_Score),color="black")+
geom_boxplot(aes(fill=LOH),outlier.shape = NA)+
xlab("")+
#coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
facet_wrap(.~ S_G_Status_BRCA1_2,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
scale_fill_manual(values=c("Biallelic Inactivation"="blue4", "Heterozygous"="grey"), breaks=c("Biallelic Inactivation","Heterozygous"))+
theme(panel.spacing = unit(6, "lines"))+
#theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=12, face= "bold"),axis.title.x=element_text(size=12))+
theme(axis.text.y=element_text(size=16),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=16))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
###violin plot subtypes of variants
all_HRD_plots_4<-all_HRD_plots[!is.na(all_HRD_plots$Variant_Subtype),]
all_HRD_plots_4_BRCAm<-subset(all_HRD_plots_4,all_HRD_plots_4$Status =="tBRCAm")
all_HRD_plots_4_BRCAm$Variant_Subtype <- factor(all_HRD_plots_4_BRCAm$Variant_Subtype, levels=c( "Frameshift/Indel","Nonsense","Loss/Rearrangement","Splice","Missense")) #reordering zygosity variables to appear in this order in the barplot
#all_HRD_plots_4_BRCAm<-all_HRD_plots_4[!all_HRD_plots_4$Status_2 %in% take_out,]
ggplot(all_HRD_plots_4_BRCAm, aes(x = Variant_Subtype , y = HRD_Score),color="black")+
geom_violin(aes(fill=Variant_Subtype),size=1)+
xlab("")+
coord_flip()+
geom_jitter(color="black",shape=16, position=position_jitter(0.2))+
geom_hline(yintercept=42,linetype="dashed")+
#facet_grid(.~ Status_3,scales="free")+
scale_y_continuous(limits = c(-1, 101), breaks = seq(0, 101, by = 25))+
#scale_fill_manual(values=c("gBRCA1m"="blueviolet","gBRCA2m"="blueviolet","sBRCA1m"="cyan3","sBRCA2m"="cyan3",`s/g BRCA not determined BRCA1m`= "grey50",`s/g BRCA not determined BRCA2m`= "grey50",`HRRm and non-BRCAm` ="orange","Non-HRRm" ="forestgreen"))+
theme_bw()+
theme(strip.text.x = element_text(size=15, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))+
theme(axis.text.x=element_text(size=14),axis.title.x=element_text(size=20,face="bold"))+
theme(axis.text.y=element_text(size=16),axis.title.y =element_text(size=20,face="bold"))+
theme(legend.title=element_text(size=19,face="bold"))+
theme(legend.text=element_text(size=16))+
ylab("HRD Score")+
theme(legend.position = 'top')+
guides(fill=guide_legend(title=""))
###LOH data
all_HRD_zyg<-all_HRD[!all_HRD$LOH =="Unknown",]
all_HRD_zyg_1<-all_HRD_zyg[!all_HRD_zyg$LOH =="",]
all_HRD_zyg_2<-all_HRD_zyg_1[!is.na(all_HRD_zyg_1$LOH),]
all_HRD_zyg_2$GENE_2<-ifelse(all_HRD_zyg_2$GENE =="BRCA1;BRCA1", "BRCA1",
ifelse(all_HRD_zyg_2$GENE =="BRCA2;BRCA2", "BRCA2",
ifelse(all_HRD_zyg_2$GENE =="CDK12;CDK12", "CDK12",as.character(all_HRD_zyg_2$GENE))))
all_1<-ddply(all_HRD_zyg_2, .(all_HRD_zyg_2$GENE_2,all_HRD_zyg_2$LOH),nrow)
colnames(all_1)<-c("Gene","Zygosity","Count") ##renaming column names in object HRR_genes_Z_1 for formatting purposes
dd4=as.data.table(all_1) %>% # creatinga data table (dd3) that splits object (HRR_genes_Z_1) into separate columns specific to each of the 4 zygosity variables
dcast(Gene~Zygosity,value.var="Count")
### adding additional columns to data table (dd3) where the proportion of Biallelic Inactivation,Suspected Biallelic Inactivation,Heterozygous and unknown variables are calculated based on the total count of each of these cases
dd4[,Biallelic.Inactivation_frac:=round((`Biallelic Inactivation`/sum(na.rm =T, `Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ### (Biallelic_Inactivation_frac) is the fraction of Biallelic Inactivation cases relative to the sum of all zygosity cases
dd4[,Heterozygous_frac:=round((Heterozygous/sum(na.rm =T,`Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ##(Heterozygous_frac) is the fraction of Heterozygous cases relative to the sum of all zygosity cases
dd_df4<-data.frame(dd4) ##reformatting the data table (dd3) into a dataframe
dd_df4_melt<-melt(dd_df4, id="Gene") # reformatting the (dd_df3) object into a molten dataframe. This is for ease of plotting purposes
dd_df4_melt_ord<-dd_df4_melt[order(dd_df4_melt$Gene),] ##Ordering the molten dataframe (dd_df3_melt) by Gene,expressed as new object (dd_df3_melt_ord)
dd_df4_melt_ord_frac<-subset(dd_df4_melt_ord,dd_df4_melt_ord$variable %like% "frac") ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df4_melt_ord_count<-dd_df4_melt_ord[!dd_df4_melt_ord$variable %like% "frac",] ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df4_melt_ord_frac$variable<-gsub("_frac","",dd_df4_melt_ord_frac$variable) ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df4_melt_ord_frac$key<-paste(dd_df4_melt_ord_frac$Gene,dd_df4_melt_ord_frac$variable,sep="_")
dd_df4_melt_ord_count$key<-paste(dd_df4_melt_ord_count$Gene,dd_df4_melt_ord_count$variable,sep="_")
dd_df4_melt_ord_frac_merged<-merge(dd_df4_melt_ord_frac,dd_df4_melt_ord_count,by="key")
dd_df4_melt_ord_frac<-dd_df4_melt_ord_frac_merged[,c(2:4,7)]
dd_df4_melt_ord_frac_new<-dd_df4_melt_ord_frac[!is.na(dd_df4_melt_ord_frac$value.x),] #creating a new object (dd_df3_melt_ord_frac_new) where missing values (NA) in the "value" column are removed from (dd_df3_melt_ord_frac) to help with plotting
#dd_df3_melt_ord_frac_new$variable<-gsub("_frac","",dd_df3_melt_ord_frac_new$variable) ##removing the "frac" from the name of each variable in object (dd_df3_melt_ord_frac_new) to help with plotting
colnames(dd_df4_melt_ord_frac_new)<-c("Gene","Zygosity","Value","Count") #renaming columns to help with plotting
dd_df4_melt_ord_frac_new$Zygosity<-gsub("Biallelic.Inactivation","Biallelic Inactivation",dd_df4_melt_ord_frac_new$Zygosity)
dd_df4_melt_ord_frac_new$Zygosity <- factor(dd_df4_melt_ord_frac_new$Zygosity, levels=c("Heterozygous","Biallelic Inactivation")) #reordering zygosity variables to appear in this order in the barplot
dd_df4_melt_ord_frac_new$Gene <- factor(dd_df4_melt_ord_frac_new$Gene, levels=c("BRCA1","BRCA2","CDK12","BRIP1","RAD51C","RAD51D","ATM","PALB2","RAD51B","FANCL","CHEK2","RAD54L","BARD1","PPP2R2A")) #reordering HRR Gene variables to appear in this order in the barplot
dd_df4_melt_ord_frac_new$Status<-ifelse(dd_df4_melt_ord_frac_new$Gene %in% BRCA,"tBRCAm","HRRm and non-BRCAm")
dd_df4_melt_ord_frac_new$Status <- gsub("HRRm and non-BRCAm","Non-BRCA HRRm", dd_df4_melt_ord_frac_new$Status)
dd_df4_melt_ord_frac_new$Status <- factor(dd_df4_melt_ord_frac_new$Status, levels=c("tBRCAm","Non-BRCA HRRm")) #reordering zygosity variables to appear in this order in the barplot
#geom_text(aes(label = paste0(Freq,"\n (", Perc,"%",")")), size = 9,color="white", position = position_stack(vjust = 0.45))+
ggplot(dd_df4_melt_ord_frac_new, aes(x = Gene, y = Value, fill = Zygosity))+
geom_bar(stat = "identity",color="black",size=0.1,width =0.7)+
xlab("\nGene")+
ylab("Percent (%)\n")+
#geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))
scale_fill_manual(values=c("Biallelic Inactivation"="dodgerblue", "Heterozygous"="maroon4"), breaks=c("Biallelic Inactivation","Heterozygous"))+
geom_text(aes(label = paste0(Count,"\n (", Value,"%",")")), size = 6.9,color="white", position = position_stack(vjust = 0.45))+
facet_grid(.~ Status,scales="free",space="free")+
theme_bw()+
theme(panel.spacing = unit(3, "lines"))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=3, linetype="solid"))+
theme(axis.text=element_text(size=24),axis.title.y=element_text(size=24,face="bold"),axis.title.x=element_blank())+
theme(legend.title=element_text(size=24,face="bold"))+
theme(legend.text=element_text(size=24))+
theme(legend.position = 'top')
#####plotting count rather than proportion
ggplot(dd_df4_melt_ord_frac_new, aes(x = Gene, y = Count, fill = Zygosity)) +
geom_bar(stat = "identity",color="black",size=0.1,width =0.7,alpha=0.8)+
xlab("\nGene")+
ylab("Number of HRRm cases with evaluable zygosity\n")+
#geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))
scale_fill_manual(values=c("Biallelic Inactivation"="dodgerblue", "Heterozygous"="maroon4"), breaks=c("Biallelic Inactivation","Heterozygous"))+
facet_grid(.~ Status,scales="free",space="free")+
theme_bw()+
theme(panel.spacing = unit(3, "lines"))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=3, linetype="solid"))+
theme(axis.text=element_text(size=18),axis.title=element_text(size=18,face="bold"))+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
write.csv(dd4,file="/Documents/Clinical_studies/Lynparza/Olaparib_Ovarian/Cross_study/gsLOH_no_unknowns_1plot.txt",row.names=FALSE)
#####now do study by study
all_HRD_zyg_2_PAOLA<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="PAOLA")
all_HRD_zyg_2_OP<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="OPINION")
all_HRD_zyg_2_L<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="LIGHT")
all_HRD_zyg_2_19<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="Study 19")
all_HRD_zyg_2_S1<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="SOLO1")
all_HRD_zyg_2_S2<-subset(all_HRD_zyg_2,all_HRD_zyg_2$Study =="SOLO2")
###PAOLA
all_1_P<-ddply(all_HRD_zyg_2_PAOLA, .(all_HRD_zyg_2_PAOLA$GENE_2,all_HRD_zyg_2_PAOLA$LOH),nrow)
colnames(all_1_P)<-c("Gene","Zygosity","Count") ##renaming column names in object HRR_genes_Z_1 for formatting purposes
dd5=as.data.table(all_1_P) %>% # creatinga data table (dd3) that splits object (HRR_genes_Z_1) into separate columns specific to each of the 4 zygosity variables
dcast(Gene~Zygosity,value.var="Count")
### adding additional columns to data table (dd3) where the proportion of Biallelic Inactivation,Suspected Biallelic Inactivation,Heterozygous and unknown variables are calculated based on the total count of each of these cases
dd5[,Biallelic.Inactivation_frac:=round((`Biallelic Inactivation`/sum(na.rm =T, `Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ### (Biallelic_Inactivation_frac) is the fraction of Biallelic Inactivation cases relative to the sum of all zygosity cases
dd5[,Heterozygous_frac:=round((Heterozygous/sum(na.rm =T,`Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ##(Heterozygous_frac) is the fraction of Heterozygous cases relative to the sum of all zygosity cases
dd_df5<-data.frame(dd5) ##reformatting the data table (dd3) into a dataframe
dd_df5_melt<-melt(dd_df5, id="Gene") # reformatting the (dd_df3) object into a molten dataframe. This is for ease of plotting purposes
dd_df5_melt_ord<-dd_df5_melt[order(dd_df5_melt$Gene),] ##Ordering the molten dataframe (dd_df3_melt) by Gene,expressed as new object (dd_df3_melt_ord)
dd_df5_melt_ord_frac<-subset(dd_df5_melt_ord,dd_df5_melt_ord$variable %like% "frac") ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df5_melt_ord_count<-dd_df5_melt_ord[!dd_df5_melt_ord$variable %like% "frac",] ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df5_melt_ord_frac$variable<-gsub("_frac","",dd_df5_melt_ord_frac$variable) ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df5_melt_ord_frac$key<-paste(dd_df5_melt_ord_frac$Gene,dd_df5_melt_ord_frac$variable,sep="_")
dd_df5_melt_ord_count$key<-paste(dd_df5_melt_ord_count$Gene,dd_df5_melt_ord_count$variable,sep="_")
dd_df5_melt_ord_frac_merged<-merge(dd_df5_melt_ord_frac,dd_df5_melt_ord_count,by="key")
dd_df5_melt_ord_frac<-dd_df5_melt_ord_frac_merged[,c(2:4,7)]
dd_df5_melt_ord_frac_new<-dd_df5_melt_ord_frac[!is.na(dd_df5_melt_ord_frac$value.x),] #creating a new object (dd_df3_melt_ord_frac_new) where missing values (NA) in the "value" column are removed from (dd_df3_melt_ord_frac) to help with plotting
#dd_df3_melt_ord_frac_new$variable<-gsub("_frac","",dd_df3_melt_ord_frac_new$variable) ##removing the "frac" from the name of each variable in object (dd_df3_melt_ord_frac_new) to help with plotting
colnames(dd_df5_melt_ord_frac_new)<-c("Gene","Zygosity","Value","Count") #renaming columns to help with plotting
dd_df5_melt_ord_frac_new$Zygosity<-gsub("Biallelic.Inactivation","Biallelic Inactivation",dd_df5_melt_ord_frac_new$Zygosity)
dd_df5_melt_ord_frac_new$Zygosity <- factor(dd_df5_melt_ord_frac_new$Zygosity, levels=c("Heterozygous","Biallelic Inactivation")) #reordering zygosity variables to appear in this order in the barplot
dd_df5_melt_ord_frac_new$Gene <- factor(dd_df5_melt_ord_frac_new$Gene, levels=c("BRCA1","BRCA2","CDK12","RAD51C","RAD51D","BRIP1","ATM","PALB2","CHEK2","RAD51B","FANCL","RAD54L","BARD1","PPP2R2A")) #reordering HRR Gene variables to appear in this order in the barplot
ggplot(dd_df5_melt_ord_frac_new, aes(x = Gene, y = Value, fill = Zygosity)) +
geom_bar(stat = "identity",color="black",size=0.1,width =0.7,alpha=0.8)+
xlab("\nGene")+
ylab("Percent (%)\n")+
geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))+
scale_fill_manual(values=c("Biallelic Inactivation"="blue4", "Heterozygous"="grey"), breaks=c("Biallelic Inactivation","Heterozygous"))+
theme_classic()+
theme(legend.position = "top")+
theme(legend.text=element_text(size=14))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=10, linetype="solid"))+
theme(axis.text=element_text(size=18),axis.title.y=element_text(size=18,face="bold"),axis.title.x=element_blank())+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
axis.title.x=element_blank()
###OPINION
all_1_O<-ddply(all_HRD_zyg_2_OP, .(all_HRD_zyg_2_OP$GENE_2,all_HRD_zyg_2_OP$LOH),nrow)
colnames(all_1_O)<-c("Gene","Zygosity","Count") ##renaming column names in object HRR_genes_Z_1 for formatting purposes
dd6=as.data.table(all_1_O) %>% # creatinga data table (dd3) that splits object (HRR_genes_Z_1) into separate columns specific to each of the 4 zygosity variables
dcast(Gene~Zygosity,value.var="Count")
### adding additional columns to data table (dd3) where the proportion of Biallelic Inactivation,Suspected Biallelic Inactivation,Heterozygous and unknown variables are calculated based on the total count of each of these cases
dd6[,Biallelic.Inactivation_frac:=round((`Biallelic Inactivation`/sum(na.rm =T, `Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ### (Biallelic_Inactivation_frac) is the fraction of Biallelic Inactivation cases relative to the sum of all zygosity cases
dd6[,Heterozygous_frac:=round((Heterozygous/sum(na.rm =T,`Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ##(Heterozygous_frac) is the fraction of Heterozygous cases relative to the sum of all zygosity cases
dd_df6<-data.frame(dd6) ##reformatting the data table (dd3) into a dataframe
dd_df6_melt<-melt(dd_df6, id="Gene") # reformatting the (dd_df3) object into a molten dataframe. This is for ease of plotting purposes
dd_df6_melt_ord<-dd_df6_melt[order(dd_df6_melt$Gene),] ##Ordering the molten dataframe (dd_df3_melt) by Gene,expressed as new object (dd_df3_melt_ord)
dd_df6_melt_ord_frac<-subset(dd_df6_melt_ord,dd_df6_melt_ord$variable %like% "frac") ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df6_melt_ord_count<-dd_df6_melt_ord[!dd_df6_melt_ord$variable %like% "frac",] ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df6_melt_ord_frac$variable<-gsub("_frac","",dd_df6_melt_ord_frac$variable) ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df6_melt_ord_frac$key<-paste(dd_df6_melt_ord_frac$Gene,dd_df6_melt_ord_frac$variable,sep="_")
dd_df6_melt_ord_count$key<-paste(dd_df6_melt_ord_count$Gene,dd_df6_melt_ord_count$variable,sep="_")
dd_df6_melt_ord_frac_merged<-merge(dd_df6_melt_ord_frac,dd_df6_melt_ord_count,by="key")
dd_df6_melt_ord_frac<-dd_df6_melt_ord_frac_merged[,c(2:4,7)]
dd_df6_melt_ord_frac_new<-dd_df6_melt_ord_frac[!is.na(dd_df6_melt_ord_frac$value.x),] #creating a new object (dd_df3_melt_ord_frac_new) where missing values (NA) in the "value" column are removed from (dd_df3_melt_ord_frac) to help with plotting
#dd_df3_melt_ord_frac_new$variable<-gsub("_frac","",dd_df3_melt_ord_frac_new$variable) ##removing the "frac" from the name of each variable in object (dd_df3_melt_ord_frac_new) to help with plotting
colnames(dd_df6_melt_ord_frac_new)<-c("Gene","Zygosity","Value","Count") #renaming columns to help with plotting
dd_df6_melt_ord_frac_new$Zygosity<-gsub("Biallelic.Inactivation","Biallelic Inactivation",dd_df6_melt_ord_frac_new$Zygosity)
dd_df6_melt_ord_frac_new$Zygosity <- factor(dd_df6_melt_ord_frac_new$Zygosity, levels=c("Heterozygous","Biallelic Inactivation")) #reordering zygosity variables to appear in this order in the barplot
dd_df6_melt_ord_frac_new$Gene <- factor(dd_df6_melt_ord_frac_new$Gene, levels=c("BRCA1","BRCA2","CDK12","BRIP1","RAD51C","RAD51D","ATM","PALB2","RAD51B","FANCL","CHEK2","RAD54L","BARD1","PPP2R2A")) #reordering HRR Gene variables to appear in this order in the barplot
ggplot(dd_df6_melt_ord_frac_new, aes(x = Gene, y = Value, fill = Zygosity)) +
geom_bar(stat = "identity",color="black",size=0.1,width =0.7,alpha=0.8)+
xlab("\nGene")+
ylab("Percent (%)\n")+
geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))+
scale_fill_manual(values=c("Biallelic Inactivation"="blue4", "Heterozygous"="grey"), breaks=c("Biallelic Inactivation","Heterozygous"))+
theme_classic()+
theme(legend.position = "top")+
theme(legend.text=element_text(size=14))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=10, linetype="solid"))+
theme(axis.text=element_text(size=18),axis.title=element_text(size=18,face="bold"))+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
###LIGHT
all_1_L<-ddply(all_HRD_zyg_2_L, .(all_HRD_zyg_2_L$GENE_2,all_HRD_zyg_2_L$LOH),nrow)
colnames(all_1_L)<-c("Gene","Zygosity","Count") ##renaming column names in object HRR_genes_Z_1 for formatting purposes
dd7=as.data.table(all_1_L) %>% # creatinga data table (dd3) that splits object (HRR_genes_Z_1) into separate columns specific to each of the 4 zygosity variables
dcast(Gene~Zygosity,value.var="Count")
### adding additional columns to data table (dd3) where the proportion of Biallelic Inactivation,Suspected Biallelic Inactivation,Heterozygous and unknown variables are calculated based on the total count of each of these cases
dd7[,Biallelic.Inactivation_frac:=round((`Biallelic Inactivation`/sum(na.rm =T, `Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ### (Biallelic_Inactivation_frac) is the fraction of Biallelic Inactivation cases relative to the sum of all zygosity cases
dd7[,Heterozygous_frac:=round((Heterozygous/sum(na.rm =T,`Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ##(Heterozygous_frac) is the fraction of Heterozygous cases relative to the sum of all zygosity cases
dd_df7<-data.frame(dd7) ##reformatting the data table (dd3) into a dataframe
dd_df7_melt<-melt(dd_df7, id="Gene") # reformatting the (dd_df3) object into a molten dataframe. This is for ease of plotting purposes
dd_df7_melt_ord<-dd_df7_melt[order(dd_df7_melt$Gene),] ##Ordering the molten dataframe (dd_df3_melt) by Gene,expressed as new object (dd_df3_melt_ord)
dd_df7_melt_ord_frac<-subset(dd_df7_melt_ord,dd_df7_melt_ord$variable %like% "frac") ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df7_melt_ord_count<-dd_df7_melt_ord[!dd_df7_melt_ord$variable %like% "frac",] ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df7_melt_ord_frac$variable<-gsub("_frac","",dd_df7_melt_ord_frac$variable) ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df7_melt_ord_frac$key<-paste(dd_df7_melt_ord_frac$Gene,dd_df7_melt_ord_frac$variable,sep="_")
dd_df7_melt_ord_count$key<-paste(dd_df7_melt_ord_count$Gene,dd_df7_melt_ord_count$variable,sep="_")
dd_df7_melt_ord_frac_merged<-merge(dd_df7_melt_ord_frac,dd_df7_melt_ord_count,by="key")
dd_df7_melt_ord_frac<-dd_df7_melt_ord_frac_merged[,c(2:4,7)]
dd_df7_melt_ord_frac_new<-dd_df7_melt_ord_frac[!is.na(dd_df7_melt_ord_frac$value.x),] #creating a new object (dd_df3_melt_ord_frac_new) where missing values (NA) in the "value" column are removed from (dd_df3_melt_ord_frac) to help with plotting
#dd_df3_melt_ord_frac_new$variable<-gsub("_frac","",dd_df3_melt_ord_frac_new$variable) ##removing the "frac" from the name of each variable in object (dd_df3_melt_ord_frac_new) to help with plotting
colnames(dd_df7_melt_ord_frac_new)<-c("Gene","Zygosity","Value","Count") #renaming columns to help with plotting
dd_df7_melt_ord_frac_new$Zygosity<-gsub("Biallelic.Inactivation","Biallelic Inactivation",dd_df7_melt_ord_frac_new$Zygosity)
dd_df7_melt_ord_frac_new$Zygosity <- factor(dd_df7_melt_ord_frac_new$Zygosity, levels=c("Heterozygous","Biallelic Inactivation")) #reordering zygosity variables to appear in this order in the barplot
dd_df7_melt_ord_frac_new$Gene <- factor(dd_df7_melt_ord_frac_new$Gene, levels=c("BRCA1","BRCA2","CDK12","BRIP1","RAD51C","RAD51D","ATM","PALB2","RAD51B","FANCL","CHEK2","RAD54L","BARD1","PPP2R2A")) #reordering HRR Gene variables to appear in this order in the barplot
ggplot(dd_df7_melt_ord_frac_new, aes(x = Gene, y = Value, fill = Zygosity)) +
geom_bar(stat = "identity",color="black",size=0.1,width =0.7,alpha=0.8)+
xlab("\nGene")+
ylab("Percent (%)\n")+
geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))+
scale_fill_manual(values=c("Biallelic Inactivation"="blue4", "Heterozygous"="grey"), breaks=c("Biallelic Inactivation","Heterozygous"))+
theme_classic()+
theme(legend.position = "top")+
theme(legend.text=element_text(size=14))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=10, linetype="solid"))+
theme(axis.text=element_text(size=18),axis.title=element_text(size=18,face="bold"))+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
####Study 19
all_1_19<-ddply(all_HRD_zyg_2_19, .(all_HRD_zyg_2_19$GENE_2,all_HRD_zyg_2_19$LOH),nrow)
colnames(all_1_19)<-c("Gene","Zygosity","Count") ##renaming column names in object HRR_genes_Z_1 for formatting purposes
dd8=as.data.table(all_1_19) %>% # creatinga data table (dd3) that splits object (HRR_genes_Z_1) into separate columns specific to each of the 4 zygosity variables
dcast(Gene~Zygosity,value.var="Count")
### adding additional columns to data table (dd3) where the proportion of Biallelic Inactivation,Suspected Biallelic Inactivation,Heterozygous and unknown variables are calculated based on the total count of each of these cases
dd8[,Biallelic.Inactivation_frac:=round((`Biallelic Inactivation`/sum(na.rm =T, `Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ### (Biallelic_Inactivation_frac) is the fraction of Biallelic Inactivation cases relative to the sum of all zygosity cases
dd8[,Heterozygous_frac:=round((Heterozygous/sum(na.rm =T,`Biallelic Inactivation`,Heterozygous))*100,digits=2),Gene] ##(Heterozygous_frac) is the fraction of Heterozygous cases relative to the sum of all zygosity cases
dd_df8<-data.frame(dd8) ##reformatting the data table (dd3) into a dataframe
dd_df8_melt<-melt(dd_df8, id="Gene") # reformatting the (dd_df3) object into a molten dataframe. This is for ease of plotting purposes
dd_df8_melt_ord<-dd_df8_melt[order(dd_df8_melt$Gene),] ##Ordering the molten dataframe (dd_df3_melt) by Gene,expressed as new object (dd_df3_melt_ord)
dd_df8_melt_ord_frac<-subset(dd_df8_melt_ord,dd_df8_melt_ord$variable %like% "frac") ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df8_melt_ord_count<-dd_df8_melt_ord[!dd_df8_melt_ord$variable %like% "frac",] ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df8_melt_ord_frac$variable<-gsub("_frac","",dd_df8_melt_ord_frac$variable) ###filtering the object (dd_df3_melt_ord) for just the fractions for each zygosity variable as this is what we will plot
dd_df8_melt_ord_frac$key<-paste(dd_df8_melt_ord_frac$Gene,dd_df8_melt_ord_frac$variable,sep="_")
dd_df8_melt_ord_count$key<-paste(dd_df8_melt_ord_count$Gene,dd_df8_melt_ord_count$variable,sep="_")
dd_df8_melt_ord_frac_merged<-merge(dd_df8_melt_ord_frac,dd_df8_melt_ord_count,by="key")
dd_df8_melt_ord_frac<-dd_df8_melt_ord_frac_merged[,c(2:4,7)]
dd_df8_melt_ord_frac_new<-dd_df8_melt_ord_frac[!is.na(dd_df8_melt_ord_frac$value.x),] #creating a new object (dd_df3_melt_ord_frac_new) where missing values (NA) in the "value" column are removed from (dd_df3_melt_ord_frac) to help with plotting
#dd_df3_melt_ord_frac_new$variable<-gsub("_frac","",dd_df3_melt_ord_frac_new$variable) ##removing the "frac" from the name of each variable in object (dd_df3_melt_ord_frac_new) to help with plotting
colnames(dd_df8_melt_ord_frac_new)<-c("Gene","Zygosity","Value","Count") #renaming columns to help with plotting
dd_df8_melt_ord_frac_new$Zygosity<-gsub("Biallelic.Inactivation","Biallelic Inactivation",dd_df8_melt_ord_frac_new$Zygosity)
dd_df8_melt_ord_frac_new$Zygosity <- factor(dd_df8_melt_ord_frac_new$Zygosity, levels=c("Heterozygous","Biallelic Inactivation")) #reordering zygosity variables to appear in this order in the barplot
dd_df8_melt_ord_frac_new$Gene <- factor(dd_df8_melt_ord_frac_new$Gene, levels=c("BRCA1","BRCA2","CDK12","BRIP1","RAD51C","RAD51D","ATM","PALB2","RAD51B","FANCL","CHEK2","RAD54L","BARD1","PPP2R2A")) #reordering HRR Gene variables to appear in this order in the barplot
ggplot(dd_df8_melt_ord_frac_new, aes(x = Gene, y = Value, fill = Zygosity)) +
geom_bar(stat = "identity",color="black",size=0.1,width =0.7,alpha=0.8)+
xlab("\nGene")+
ylab("Percent (%)\n")+
geom_text(aes(label = paste0(Count," (", Value,"%",")")), size = 4.9,color="white", position = position_stack(vjust = 0.45))+
scale_fill_manual(values=c("Biallelic Inactivation"="blue4", "Heterozygous"="grey"), breaks=c("Biallelic Inactivation","Heterozygous"))+
theme_classic()+
theme(legend.position = "top")+
theme(legend.text=element_text(size=14))+
guides(color=guide_legend(title="Zygosity"))+
theme(strip.text.x = element_text(size=25, color="Black",
face="bold.italic"))+
theme(strip.background = element_rect(colour="black", fill="white",
size=10, linetype="solid"))+
theme(axis.text=element_text(size=18),axis.title=element_text(size=18,face="bold"))+
theme(legend.title=element_text(size=18,face="bold"))+
theme(legend.text=element_text(size=18))+
theme(legend.position = 'top')
####SOLO1