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130 lines
5.2 KiB
130 lines
5.2 KiB
library(tidyverse)
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library(openxlsx)
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library(ggpubr)
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path="C:/Users/47926492N/OneDrive - IDIBELL - Institut d'Investigació Biomèdica de Bellvitge/ImmunoPreserve/TestIP/"
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data<-read.xlsx(paste0(path, "Panel3.xlsx"))
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data1<-data %>% gather(phen, value, -sample, -Population)
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data1["TimePoint"]<-sapply(data1$sample, function(x) strsplit(x, "_")[[1]][2])
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data1["sample"]<-sapply(data1$sample, function(x) strsplit(x, "_")[[1]][1])
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data1<-data1 %>% group_by(Population, TimePoint, phen) %>% summarise(value=mean(value))
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data1$phen<-gsub("."," ",data1$phen, fixed = T)
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data1$phen<-gsub("[A-Z0-9]*-", "", data1$phen)
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data1$phen<-gsub(" ", " ", data1$phen)
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data1$phen<-gsub("[ ]*$", "", data1$phen)
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data1$phen<-gsub("^[ ]*", "", data1$phen)
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data1$phen[data1$phen == ""]<-"All Negative"
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data1["phen1"]<-"CD38"
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data1[!grepl("CD38+", data1$phen),"phen1"]<-NA
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data1["phen2"]<-"HLADR"
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data1[!grepl("HLADR+", data1$phen),"phen2"]<-NA
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data1["phen3"]<-"GZMB"
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data1[!grepl("GZMB+", data1$phen),"phen3"]<-NA
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data1<-data1 %>% arrange(desc(value))
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data2<-data1 %>% filter(!phen %in% c("All Negative","Other"))
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data1<-rbind(data2, data1 %>% filter(phen %in% c("All Negative","Other")) %>% arrange(desc(phen)))
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data1.list<-list()
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cont<-1
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for (i in data1$Population %>% unique){
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for (j in data1$TimePoint %>% unique){
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data_temp<-data1 %>% filter(Population == i & TimePoint == j)
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data_temp$ymax<-cumsum(data_temp$value)
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data_temp$ymin<-c(0, head(data_temp$ymax, n=-1))
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data1.list[[cont]]<-data_temp
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cont<-cont+1
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}
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}
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data1<-do.call(rbind, data1.list)
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data1<-data1 %>% filter(TimePoint %in% c("W1","W2","W8","W12"))
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data1$TimePoint<-factor(data1$TimePoint, levels=c("W1","W2","W8","W12"))
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data1$Population<-factor(data1$Population, levels=c("CD8","CD4", "NK"))
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color<-colorRampPalette(brewer.pal(8, "Set3"))(length(bool.name))
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names(color)<-bool.name %>% gsub("[A-Z0-9]*-", "", .) %>% gsub(" "," ",.) %>% gsub("[ ]*$", "", .) %>% gsub("^[ ]*", "", .) %>% gsub("^$","All Negative", .)
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color["All Negative"]<-"grey90"
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basic.color<-color[c("CD38+","HLADR+","GZMB+")]
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names(basic.color)<-c("CD38","HLADR","GZMB")
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g_coex<-ggplot(data1)+
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facet_grid(Population~TimePoint)+
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geom_rect(aes(ymax=ymax, ymin=ymin, xmax=4.5, xmin=0), fill=color[data1$phen])+
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geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.4, xmin=5, fill=factor(phen1, levels=c("CD38","HLADR","GZMB"))))+
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geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.9, xmin=5.5, fill=factor(phen2, levels=c("CD38","HLADR","GZMB"))))+
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geom_rect(aes(ymax=ymax, ymin=ymin, xmax=6.4, xmin=6, fill=factor(phen3, levels=c("CD38","HLADR","GZMB"))))+
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scale_fill_manual(values = basic.color, na.value="#FFFFFF00", drop=F, limits=c("CD38","HLADR","GZMB"), name="Activation")+
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coord_polar(theta="y") + # Try to remove that to understand how the chart is built initially
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xlim(c(0, 6.5)) +# Try to remove that to see how to make a pie chart
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theme_classic()+
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theme(strip.background = element_blank(),
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strip.text = element_text(size=12, face="bold"),
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axis.line = element_blank(),
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axis.ticks = element_blank(),
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# plot.margin = margin(-200,0,0,0),
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axis.text = element_blank())
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mtl_rec<-as.data.frame(matrix(nrow=0, ncol=3))
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colnames(mtl_rec)<-c(colnames(data1)[1:2],"phen")
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for (rec in c("CD38+", "GZMB+", "HLADR+")){
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temp<-data1[grep(rec, data1$phen),]
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if (nrow(temp) > 0){
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mtl_rec<-rbind(mtl_rec, data.frame(temp, "Rec"=rec))
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}
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}
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mtl_rec_sum<-mtl_rec %>% group_by(TimePoint,Population,Rec, phen) %>% summarise(value=sum(value))
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ggplot(mtl_rec_sum, aes(Rec, value, fill=phen))+
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facet_grid(Population~TimePoint)+
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geom_bar(stat="identity", color="black")+
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scale_fill_manual(values = color, name="IC")
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data2<-data %>% gather(phen, value, -sample, -Population)
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data2["TimePoint"]<-sapply(data2$sample, function(x) strsplit(x, "_")[[1]][2])
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data2["sample"]<-sapply(data2$sample, function(x) strsplit(x, "_")[[1]][1])
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data2$phen<-gsub("."," ",data2$phen, fixed = T)
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data2$phen<-gsub("[A-Z0-9]*-", "", data2$phen)
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data2$phen<-gsub(" ", " ", data2$phen)
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data2$phen<-gsub("[ ]*$", "", data2$phen)
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data2$phen<-gsub("^[ ]*", "", data2$phen)
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data2$phen[data2$phen == ""]<-"All Negative"
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data2<-data2 %>% filter(TimePoint %in% c("W1","W2","W8","W12"))
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mtl_rec2<-as.data.frame(matrix(nrow=0, ncol=3))
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colnames(mtl_rec2)<-c(colnames(data2)[1:2],"phen")
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for (rec in c("CD38", "GZMB", "HLADR")){
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temp<-data2[grep(rec, data2$phen),]
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if (nrow(temp) > 0){
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mtl_rec2<-rbind(mtl_rec2, data.frame(temp, "Rec"=rec))
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}
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}
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mtl_rec2<-mtl_rec2 %>% group_by(sample,Population,TimePoint,Rec) %>% summarise(value=sum(value, na.rm=T))
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mtl_rec2$Rec<-factor(mtl_rec2$Rec, levels=c("CD38", "GZMB", "HLADR"))
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mtl_rec2$TimePoint<-factor(mtl_rec2$TimePoint, levels=c("W1","W2","W8","W12"))
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mtl_rec2$Population<-factor(mtl_rec2$Population, levels=c("CD8","CD4","NK"))
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g_Rec<-ggplot(mtl_rec2, aes(TimePoint, value))+
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facet_grid(Population~Rec)+
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geom_point(color="grey80")+
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geom_line(aes(group=sample), color="grey80")+
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geom_point(stat="summary", aes(color=Rec), size=2)+
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geom_line(aes(group=Rec, color=Rec), stat="summary", size=1)+
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scale_color_manual(values = basic.color, name="IC")+
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labs(y="% CD8/CD4/NK T cells")+
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guides(color="none")+
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theme_bw()
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ggarrange(g_Rec, g_coex, ncol = 1)
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ggsave(paste0(path, "Analysis/Panel3.png"), width = 7.5, height = 12)
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