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################################################

#  File Name:Figure1_drivers.R

#  Author: Baoyan Bai





#################################################





########Manually plot driver genes (DO NOT USE GENVISR)

########date: 20211012, plot 51 genes with VAF adjusted for tumor% >=0.15

########add clinic features: group, POD24 infor

########genes ordered based on mutation frequency



library(maftools)

library(plyr)

library(dplyr)

library(pheatmap)

library(xlsx)

library(reshape)

library(readxl)

library(ggplot2)

library(RColorBrewer)

library(egg)

setwd("~/Desktop/FL/fl_latest")

flreseq<- read.table(file="16_combined/fl44_reseq_final_060619.txt",sep="\t",header = T, 

                     stringsAsFactors = F,quote="")



drivers<- read.table(file="28_drivergenes/51drivers.txt",header=T,stringsAsFactors = F)





###clinical information



clinic_biopsy<- read.table(file="2_clinic_infor/FL94biopsies_Sigve_20211125.txt",sep="\t",header=T,stringsAsFactors = F)

clinic_biopsy$type[clinic_biopsy$type=="pre&transform"]<- "transform"









clinic_biopsy_redced<- clinic_biopsy%>%select(Sample_name, type, group, POD24.1)

names(clinic_biopsy_redced)<- c("sample", "type", "group", "POD24")

clinic_biopsy_redced.melt<- melt(clinic_biopsy_redced, id.vars = "sample")

clinic_biopsy_redced.melt$variable<- factor(clinic_biopsy_redced.melt$variable, levels=c("group","POD24","type"))



####clinic information









###making data.frame for waterfall plot



plot_df_all<- unique(flreseq%>%filter(Region=="Coding"& VARIANT!="Silent")%>%select(SAMPLE, SYMBOL,VARIANT))

names(plot_df_all)<- c("sample", "gene", "variant_class")

plot_df_all$variant_class[plot_df_all$variant_class=="Frame_Shift_Ins" |plot_df_all$variant_class=="Frame_Shift_Del"]<- "Frame_Shift"

plot_df_all$variant_class[plot_df_all$variant_class=="In_Frame_Ins" |plot_df_all$variant_class=="In_Frame_Del"]<- "In_Frame"



sample_order<- c(as.character(clinic_biopsy_redced$sample[clinic_biopsy_redced$group=="nFL"]),

                 as.character(clinic_biopsy_redced$sample[clinic_biopsy_redced$group=="tFL"& clinic_biopsy_redced$type!="transform"]) ,

                 as.character(clinic_biopsy_redced$sample[clinic_biopsy_redced$group=="tFL" & clinic_biopsy_redced$type=="transform"]))



remove_sample<- c("P34_1", "P35_1", "P58_2")

sample_order_94<- setdiff(sample_order, remove_sample )















plot_df_all$variant_class[plot_df_all$variant_class=="Translation_Start_Site" |plot_df_all$variant_class=="Nonstop_Mutation"]<- "Translation_Site"



plot_df_all$variant_class[plot_df_all$variant_class=="Missense_Mutation"]<- "Missense"



plot_df_all$variant_class[plot_df_all$variant_class=="Nonsense_Mutation"]<- "Nonsense"



plot_df_all$variant_class<- factor(plot_df_all$variant_class, 

                                   levels=c("Frame_Shift","Nonsense", "Splice_Site", "In_Frame", "Translation_Site", "Missense"))



##########

plot_df<- ddply(plot_df_all, .(sample, gene), mutate, variant_class_correct=min(as.integer( variant_class )))







plot_df$variant_1<-levels(plot_df$variant_class)[plot_df$variant_class_correct]





plot_df.reduced<- unique(plot_df%>%select(sample, gene, variant_1))

plot_df.cast<- cast(plot_df.reduced, gene~ sample)



plot_df.melt<- melt(plot_df.cast, id.vars="gene")



plot_df.drivers<- subset(plot_df.melt, gene %in%drivers$SYMBOL)





plot_df.drivers$sample<-factor(plot_df.drivers$sample, levels=sample_order_94)

plot_df.drivers$value<- as.character(plot_df.drivers$value)





plot_df.drivers$value<- factor(plot_df.drivers$value, 

                               levels=c("Frame_Shift", "Nonsense","Splice_Site", "In_Frame", "Translation_Site","Missense"))





#####sort genes based on patient-level frequency



plot_df_pat<- unique(flreseq%>%filter(Region=="Coding"& VARIANT!="Silent")%>%select(Patient, SYMBOL)%>% filter(SYMBOL %in% drivers$SYMBOL))



###########



drivers.sum.pat<- ddply(plot_df_pat, .(SYMBOL), summarise,, Patient=length(Patient))



drivers.sum.pat$SYMBOL<- reorder(drivers.sum.pat$SYMBOL, drivers.sum.pat$Patient)







###

plot_df.drivers$gene<- factor(plot_df.drivers$gene, levels=levels(drivers.sum.pat$SYMBOL))





mutation<-ggplot(plot_df.drivers, aes(x=sample, y=gene,fill=value))+geom_tile(color="gray", size=0.1)+

  scale_fill_manual(values=c("#e7298a","firebrick","navyblue","#1b9e77", "#7570b3","#66a61e","white"),na.translate=FALSE)+scale_x_discrete(drop=FALSE)+

  theme(panel.background = element_blank(),

        legend.text = element_text(size=14,face="bold"),

        legend.title = element_text(size=16,face="bold"),

        axis.ticks.x=element_blank(),

        axis.ticks.y=element_blank(),

        legend.position = "none",

        axis.text.x =element_blank(),

        axis.text.y =element_text(size=10,hjust =0),

        plot.margin = unit(c(0.5,0.5,-0.2,0.5), "cm"))+ xlab("")+ylab("")

guides(fill=guide_legend(title="Mutation Type"))







#####frequency plot

pat<- unique(clinic_biopsy%>% select(Patient, group))



drivers.pat.clinic<-merge(plot_df_pat, pat,by="Patient")

drivers.pat.clinic$SYMBOL<- factor(drivers.pat.clinic$SYMBOL, levels=levels(plot_df.drivers$gene))

drivers.pat.clinic$group<- factor(drivers.pat.clinic$group, levels=c("tFL", "nFL"))



plot_freq<- ggplot(drivers.pat.clinic, aes(x=SYMBOL, fill=group))+ geom_bar()+  scale_fill_manual(values=c("red","blue"))+

  coord_flip()+theme_bw()+ theme(axis.text.y = element_blank() ,

                                 axis.text.x=element_text(size=12),

                                 legend.position = c(0.80, 0.1),

                                 axis.ticks.y=element_blank(),

                                 plot.margin = unit(c(0.3,0.3,-0.8,-0.5), "cm"))+

  ylab("Number of patients")+xlab("")



freq<-plot_freq+ guides(fill = guide_legend(reverse=TRUE))











#######clinic information

clinic_biopsy_redced.melt$sample<- factor(clinic_biopsy_redced.melt$sample, levels=sample_order_94)

clinic.final<- subset(clinic_biopsy_redced.melt,sample %in%sample_order_94)

clinic.final$variable<- as.character(clinic.final$variable)



clinic.final$variable[clinic.final$variable=="group"]<- "Group"

clinic.final$variable[clinic.final$variable=="type"]<- "Biopsy"

clinic.final$variable<- factor(clinic.final$variable,levels=c("Group", "POD24", "Biopsy"))



clinic.final$value<- factor(clinic.final$value, levels=c("pre", "relapse", "transform", "nFL", "tFL","0", "1"))

clinic.plot<- ggplot(clinic.final, aes(x=sample, y=variable, fill=value))+geom_tile()+

  scale_fill_manual(values=c("dark cyan","#fbb4ae", "purple","#998EC3","#F1A340", "grey", "black"))+

  theme(panel.background = element_blank(),

        axis.ticks.x=element_blank(),

        legend.text = element_text(size=12,face="bold"),

        legend.title = element_text(size=14,face="bold"),

        axis.ticks.y=element_blank(),

        axis.text.x=element_blank(),

        legend.position="right",

        axis.text.y =element_text(size=14,face="bold"),

        plot.margin = unit(c(0.1,0.2,0,2,0.5), "cm"))+ xlab("")+ylab("")+

  guides(fill=guide_legend(ncol=2, title="Biopsy      Group"))



###select colors



"#998EC3","#F1A340",

     

clinic.plot.nolegend<- ggplot(clinic.final, aes(x=sample, y=variable, fill=value))+geom_tile()+

  scale_fill_manual(values=c("dark cyan","#fbb4ae", "purple","#998EC3","#F1A340", "grey", "black"))+

  theme(panel.background = element_blank(),

        axis.ticks.x=element_blank(),

        legend.text = element_text(size=12,face="bold"),

        legend.title = element_text(size=14,face="bold"),

        axis.ticks.y=element_blank(),

        axis.text.x=element_blank(),

        legend.position="null",

        axis.text.y =element_text(size=12),

        plot.margin = unit(c(-0.2,0.5,0.5,0.5), "cm"))+ xlab("")+ylab("")



##########



#####arrange the plots





pdf(file="figures/Final_figures/51drivers_complete_clinic_20211130.pdf", width=13,height=8)

ggarrange(mutation,  clinic.plot.nolegend, nrow=2, ncol=1,  heights=c(10,1))



dev.off()



###########stop



#####making forest plot

clinic<- read_excel("./2_clinic_infor/FL_Baoyan_clinical_file_FINAL_020719_use.xlsx", sheet = 1)



clinic$group<- ifelse(is.na(clinic$Date_transf), "nFL", "tFL")



clinic.info<- clinic%>%select(Patient_ID, group)



names(clinic.info)<- c("Patient", "Group")



flreseq$chrom<- as.character(lapply(strsplit(as.character(flreseq$VAR_ID), "\\_"),"[",1))

flreseq$pos<-as.character(lapply(strsplit(as.character(flreseq$VAR_ID), "\\_"),"[",2))

flreseq$ref<-as.character(lapply(strsplit(as.character(flreseq$VAR_ID), "\\_"),"[",3))

flreseq$alt<-as.character(lapply(strsplit(as.character(flreseq$VAR_ID), "\\_"),"[",4))







flreseq_sample.maf<- unique(flreseq%>%select(Patient, SYMBOL, chrom, pos, ref, alt, VARIANT,VARIANT_CLASS, HGVSp_short))







names(flreseq_sample.maf)<- c("Tumor_Sample_Barcode",

                              "Hugo_Symbol", "Chromosome", "Start_Position", "Reference_Allele", 

                              "Tumor_Seq_Allele2", "Variant_Classification", "Variant_Type", "HGVSp_short")



flreseq_sample.maf$End_Position<- flreseq_sample.maf$Start_Position  

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="snv"]<- "SNP"

flreseq_sample.maf$Variant_Classification[flreseq_sample.maf$Variant_Classification=="3' UTR"]<- "3'UTR"

flreseq_sample.maf$Variant_Classification[flreseq_sample.maf$Variant_Classification=="5' UTR"]<- "5'UTR"





flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="deletion"]<- "DEL"

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="inDEL"]<- "DEL"

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="insertion"]<- "INS"

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="INdel"]<- "INS"

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="substitution"]<- "SUB"

flreseq_sample.maf$Variant_Type[flreseq_sample.maf$Variant_Type=="SNV"]<- "SNP"

#######seperate nFL and tFL







flreseq.nFL<- subset(flreseq_sample.maf, Tumor_Sample_Barcode%in% clinic.info$Patient[clinic.info$Group=="nFL"])

flreseq.tFL<- subset(flreseq_sample.maf, Tumor_Sample_Barcode%in% clinic.info$Patient[clinic.info$Group=="tFL"])



###taking only drivers

flreseq.nFL.drivers<- flreseq.nFL%>%filter (Hugo_Symbol%in% drivers$SYMBOL)

flreseq.tFL.drivers<- flreseq.tFL%>%filter (Hugo_Symbol%in% drivers$SYMBOL)



#####





nFL.maf<- read.maf(flreseq.nFL.drivers)

tFL.maf<- read.maf(flreseq.tFL.drivers)

nFL.vs.tFL<- mafCompare(m1=nFL.maf, m2=tFL.maf, m1Name = "nFL group", m2Name = "tFL group", minMut=0)

print(nFL.vs.tFL)







drivers.sum.pat$SYMBOL<- reorder(drivers.sum.pat$SYMBOL, drivers.sum.pat$Patient)



trace(forestPlot, edit=TRUE)

###change

#m.sigs$Hugo_Symbol <- factor(m.sigs$Hugo_Symbol, levels = levels(drivers.sum.pat$SYMBOL))

#m.sigs = m.sigs[order(m.sigs$Hugo_Symbol)]



#m.sigs$or_new = ifelse(test = m.sigs$or > 3, yes = 3, no = m.sigs$or)

#m.sigs$upper = ifelse(test = m.sigs$ci.up > 3, yes = 3, 

#                      no = m.sigs$ci.up)

#m.sigs$lower = ifelse(test = m.sigs$ci.low > 3, yes = 3, 

#                      no = m.sigs$ci.low)



#####arrage



#if (is.null(fdr)) {

#  m.sigs = mafCompareRes$results[pval < pVal]

#}

#else {

#  m.sigs = mafCompareRes$results[adjPval < fdr]

#}



pdf(file="2021_codes/figures/51drivers_complete_clinic_20211125.pdf", width=13, height=8)

ggarrange(mutation,  clinic.plot.nolegend, nrow=2,   heights=c(8.5,1))



dev.off()



pdf(file="figures/51drivers_forest_20211130.pdf",width=6, height=10)

forestPlot(mafCompareRes = nFL.vs.tFL, color=c("#F1A340","#998EC3"),geneFontSize = 1,pVal = 1)

dev.off()







##################################

flreseq.ns<- unique(flreseq%>%filter(Region=="Coding" & VARIANT !="Silent")%>%select(Patient, SYMBOL))

clinic<- read.xlsx(file="2_clinic_infor/FL_Baoyan_clinical_file_FINAL_020719 - survival.xlsx",sheetIndex = 1,header=T)

fl.drivers<- subset(flreseq.ns, SYMBOL %in% drivers$Genes)



clinic$trans<- ifelse(is.na(clinic$Date_transf),"0","1")

patient<- clinic%>%select(Patient_ID, trans)

names(patient)<- c("Patient", "Trans_status")



fl.drivers.clinic<- merge(fl.drivers, patient, by="Patient")

fl.drivers.clinic$SYMBOL_ordered<- factor(fl.drivers.clinic$SYMBOL, levels=drivers$Genes)



fl.drivers.summary<-ddply(fl.drivers.clinic,.(SYMBOL, Trans_status), summarise, num_pat=length(Patient))



fl.drivers.sum.cast<- cast(fl.drivers.summary, SYMBOL~ Trans_status)

fl.drivers.sum.cast[is.na(fl.drivers.sum.cast)]<-0



fl.drivers.melt<- melt(fl.drivers.sum.cast, id.vars = 'SYMBOL')



fl.drivers.melt$value[fl.drivers.melt$Trans_status=="0"]<- fl.drivers.melt$value[fl.drivers.melt$Trans_status=="0"]*-1



fl.drivers.melt$SYMBOL<- factor(fl.drivers.melt$SYMBOL, levels=rev(drivers$Genes))











mutation_nolegend<-mutation<-ggplot(plot_df.drivers, aes(x=sample, y=gene,fill=value))+geom_tile(color="gray", size=0.1)+

  scale_fill_manual(values=c("#e7298a","firebrick","navyblue","#1b9e77", "#7570b3","#66a61e","white"),na.translate=FALSE)+scale_x_discrete(drop=FALSE)+

  theme(panel.background = element_blank(),

        legend.text = element_text(size=14,face="bold"),

        legend.title = element_text(size=16,face="bold"),

        axis.ticks.x=element_blank(),

        legend.position = "",

        axis.text.x =element_blank(),

        axis.ticks.y=element_blank(),

        axis.text.y=element_blank(),

        

        plot.margin = unit(c(0.2,-0.2,-0.5,0.1), "cm"))+ xlab("")+ylab("")+

  

  guides(fill=guide_legend(title="Mutation Type"))



clinic.nolegend<- ggplot(clinic.final, aes(x=sample, y=variable, fill=value))+geom_tile()+

  scale_fill_manual(values=c("dark cyan","#fbb4ae", "purple","blue", "red"))+

  theme(panel.background = element_blank(),

        axis.ticks.x=element_blank(),

        legend.text = element_text(size=14,face="bold"),

        legend.title = element_text(size=16,face="bold"),

        axis.ticks.y=element_blank(),

        axis.text.x=element_blank(),

        legend.position="",

        axis.text.y =element_text(size=16,face="bold"),

        plot.margin = unit(c(-0.5,0.2,0,2,0.1), "cm"))+ xlab("")+ylab("")+

  guides(fill=guide_legend(ncol=2, title="Biopsy      Group"))





















mutation_legend<-mutation<-ggplot(plot_df.drivers, aes(x=sample, y=gene,fill=value))+geom_tile(color="gray", size=0.1)+

  scale_fill_manual(values=c("#e7298a","firebrick","navyblue","#1b9e77", "#7570b3","#66a61e","white"),na.translate=FALSE)+scale_x_discrete(drop=FALSE)+

  theme(panel.background = element_blank(),

        legend.text = element_text(size=14,face="bold"),

        legend.title = element_text(size=16,face="bold"),

        axis.ticks.x=element_blank(),

        legend.position = "left",

        axis.text.x =element_blank(),

        axis.ticks.y=element_blank(),

        axis.text.y=element_blank(),

        

        plot.margin = unit(c(0.2,-0.2,-0.5,0.1), "cm"))+ xlab("")+ylab("")+

  

  guides(fill=guide_legend(title="Mutation Type"))





clinic.legend<- ggplot(clinic.final, aes(x=sample, y=variable, fill=value))+geom_tile()+

  scale_fill_manual(values=c("dark cyan","#fbb4ae", "purple","blue", "red"))+

  theme(panel.background = element_blank(),

        axis.ticks.x=element_blank(),

        legend.text = element_text(size=14,face="bold"),

        legend.title = element_text(size=16,face="bold"),

        axis.ticks.y=element_blank(),

        axis.text.x=element_blank(),

        legend.position="left",

        axis.text.y =element_text(size=16,face="bold"),

        plot.margin = unit(c(-0.5,0.2,0,2,0.1), "cm"))+ xlab("")+ylab("")+

  guides(fill=guide_legend(ncol=2, title="Biopsy      Group"))