library(tidyverse)
library(openxlsx)
library(ggpubr)

path="C:/Users/47926492N/OneDrive - IDIBELL - Institut d'Investigació Biomèdica de Bellvitge/ImmunoPreserve/TestIP/"
data<-read.xlsx(paste0(path, "Panel3.xlsx"))

data1<-data %>% gather(phen, value, -sample, -Population)
data1["TimePoint"]<-sapply(data1$sample, function(x) strsplit(x, "_")[[1]][2])
data1["sample"]<-sapply(data1$sample, function(x) strsplit(x, "_")[[1]][1])
data1<-data1 %>% group_by(Population, TimePoint, phen) %>% summarise(value=mean(value))

data1$phen<-gsub("."," ",data1$phen, fixed = T)
data1$phen<-gsub("[A-Z0-9]*-", "", data1$phen)
data1$phen<-gsub("  ", " ", data1$phen)
data1$phen<-gsub("[ ]*$", "", data1$phen)
data1$phen<-gsub("^[ ]*", "", data1$phen)
data1$phen[data1$phen == ""]<-"All Negative"

data1["phen1"]<-"CD38"
data1[!grepl("CD38+", data1$phen),"phen1"]<-NA

data1["phen2"]<-"HLADR"
data1[!grepl("HLADR+", data1$phen),"phen2"]<-NA

data1["phen3"]<-"GZMB"
data1[!grepl("GZMB+", data1$phen),"phen3"]<-NA

data1<-data1 %>% arrange(desc(value))
data2<-data1 %>% filter(!phen %in% c("All Negative","Other"))
data1<-rbind(data2, data1 %>% filter(phen %in% c("All Negative","Other")) %>% arrange(desc(phen)))

data1.list<-list()
cont<-1
for (i in data1$Population %>% unique){
  for (j in data1$TimePoint %>% unique){
    data_temp<-data1 %>% filter(Population == i & TimePoint == j)
    data_temp$ymax<-cumsum(data_temp$value)
    data_temp$ymin<-c(0, head(data_temp$ymax, n=-1))
    data1.list[[cont]]<-data_temp
    cont<-cont+1
  }
}
data1<-do.call(rbind, data1.list)

data1<-data1 %>% filter(TimePoint %in% c("W1","W2","W8","W12"))

data1$TimePoint<-factor(data1$TimePoint, levels=c("W1","W2","W8","W12"))
data1$Population<-factor(data1$Population, levels=c("CD8","CD4", "NK"))

color<-colorRampPalette(brewer.pal(8, "Set3"))(length(bool.name))
names(color)<-bool.name %>% gsub("[A-Z0-9]*-", "", .) %>% gsub("  "," ",.) %>% gsub("[ ]*$", "", .) %>% gsub("^[ ]*", "", .) %>% gsub("^$","All Negative", .)
color["All Negative"]<-"grey90"
basic.color<-color[c("CD38+","HLADR+","GZMB+")]
names(basic.color)<-c("CD38","HLADR","GZMB")

g_coex<-ggplot(data1)+
  facet_grid(Population~TimePoint)+
  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=4.5, xmin=0), fill=color[data1$phen])+
  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.4, xmin=5, fill=factor(phen1, levels=c("CD38","HLADR","GZMB"))))+
  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.9, xmin=5.5, fill=factor(phen2, levels=c("CD38","HLADR","GZMB"))))+
  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=6.4, xmin=6, fill=factor(phen3, levels=c("CD38","HLADR","GZMB"))))+
  scale_fill_manual(values = basic.color, na.value="#FFFFFF00", drop=F, limits=c("CD38","HLADR","GZMB"), name="Activation")+
  coord_polar(theta="y") + # Try to remove that to understand how the chart is built initially
  xlim(c(0, 6.5)) +# Try to remove that to see how to make a pie chart
  theme_classic()+
  theme(strip.background = element_blank(),
        strip.text = element_text(size=12, face="bold"),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        # plot.margin = margin(-200,0,0,0),
        axis.text = element_blank())

mtl_rec<-as.data.frame(matrix(nrow=0, ncol=3))
colnames(mtl_rec)<-c(colnames(data1)[1:2],"phen")
for (rec in c("CD38+", "GZMB+", "HLADR+")){
  temp<-data1[grep(rec, data1$phen),]
  if (nrow(temp) > 0){
    mtl_rec<-rbind(mtl_rec, data.frame(temp, "Rec"=rec))
  }
}

mtl_rec_sum<-mtl_rec %>% group_by(TimePoint,Population,Rec, phen) %>% summarise(value=sum(value))

ggplot(mtl_rec_sum, aes(Rec, value, fill=phen))+
  facet_grid(Population~TimePoint)+
  geom_bar(stat="identity", color="black")+
  scale_fill_manual(values = color, name="IC")

data2<-data %>% gather(phen, value, -sample, -Population)
data2["TimePoint"]<-sapply(data2$sample, function(x) strsplit(x, "_")[[1]][2])
data2["sample"]<-sapply(data2$sample, function(x) strsplit(x, "_")[[1]][1])

data2$phen<-gsub("."," ",data2$phen, fixed = T)
data2$phen<-gsub("[A-Z0-9]*-", "", data2$phen)
data2$phen<-gsub("  ", " ", data2$phen)
data2$phen<-gsub("[ ]*$", "", data2$phen)
data2$phen<-gsub("^[ ]*", "", data2$phen)
data2$phen[data2$phen == ""]<-"All Negative"

data2<-data2 %>% filter(TimePoint %in% c("W1","W2","W8","W12"))

mtl_rec2<-as.data.frame(matrix(nrow=0, ncol=3))
colnames(mtl_rec2)<-c(colnames(data2)[1:2],"phen")
for (rec in c("CD38", "GZMB", "HLADR")){
  temp<-data2[grep(rec, data2$phen),]
  if (nrow(temp) > 0){
    mtl_rec2<-rbind(mtl_rec2, data.frame(temp, "Rec"=rec))
  }
}

mtl_rec2<-mtl_rec2 %>% group_by(sample,Population,TimePoint,Rec) %>% summarise(value=sum(value, na.rm=T))

mtl_rec2$Rec<-factor(mtl_rec2$Rec, levels=c("CD38", "GZMB", "HLADR"))
mtl_rec2$TimePoint<-factor(mtl_rec2$TimePoint, levels=c("W1","W2","W8","W12"))
mtl_rec2$Population<-factor(mtl_rec2$Population, levels=c("CD8","CD4","NK"))

g_Rec<-ggplot(mtl_rec2, aes(TimePoint, value))+
  facet_grid(Population~Rec)+
  geom_point(color="grey80")+
  geom_line(aes(group=sample), color="grey80")+
  geom_point(stat="summary", aes(color=Rec), size=2)+
  geom_line(aes(group=Rec, color=Rec), stat="summary", size=1)+
  scale_color_manual(values = basic.color, name="IC")+
  labs(y="% CD8/CD4/NK T cells")+
  guides(color="none")+
  theme_bw()

ggarrange(g_Rec, g_coex, ncol = 1)
ggsave(paste0(path, "Analysis/Panel3.png"), width = 7.5, height = 12)