Scripts de análisis de IC y Panel3

IC-coex.R

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+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, "Panel1.xlsx"), sheet = "IC")
+
+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$phen<-gsub("p","+",data1$phen)
+data1$phen<-gsub("n","-",data1$phen)
+data1$phen<-gsub("_"," ",data1$phen)
+
+data1$phen<-gsub("n","-",data1$phen, fixed = T)
+data1$phen<-gsub("p","+",data1$phen, fixed = T)
+data1$phen<-gsub("_"," ",data1$phen)
+data1[data1$value < 1, "phen"]<-"Other"
+data1$phen<-gsub("[A-Z]*-*[0-9T]- *", "", data1$phen)
+data1$phen<-gsub("+ $", "", data1$phen)
+data1$phen[data1$phen == ""]<-"All Negative"
+
+data1["phen1"]<-"PD1"
+data1[!grepl("PD1+", data1$phen),"phen1"]<-NA
+
+data1["phen2"]<-"TIM3"
+data1[!grepl("TIM3+", data1$phen),"phen2"]<-NA
+
+data1["phen3"]<-"CTLA4"
+data1[!grepl("CTLA4+", data1$phen),"phen3"]<-NA
+
+data1["phen4"]<-"LAG3"
+data1[!grepl("LAG3+", data1$phen),"phen4"]<-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"))
+
+color<-c(c("CTLA4+ LAG3+ PD1+ TIM3+"="black","All Negative"="grey90","Other"="grey50", "PD1+"="#C07AFF", "CTLA4+"="#3EB3DE","TIM3+"="#5EF551","LAG3+"="#DEBB3E"),
+         c("CTLA4+ PD1+"="#6666FF","PD1+ TIM3+"="#849CA8", "LAG3+ PD1+"="#C47F9F", "CTLA4+ TIM3+"="#4ED498", "CTLA4+ LAG3+"="#8EB78E", "LAG3+ TIM3+"="#9ED848"),
+         c("CTLA4+ PD1+ TIM3+"="#B81515", "LAG3+ PD1+ TIM3+"="#0f5860"))
+
+basic.color<-color[c("PD1+","TIM3+","CTLA4+","LAG3+")]
+names(basic.color)<-c("PD1","TIM3","CTLA4","LAG3")
+# Make the plot
+g_coex<-ggplot(data1)+
+  facet_grid(Population~TimePoint)+
+  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=4.5, xmin=0), fill=color[data1$phen])+
+  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.4, xmin=5, fill=factor(phen1, levels=c("PD1","TIM3","CTLA4","LAG3"))))+
+  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=5.9, xmin=5.5, fill=factor(phen2, levels=c("PD1","TIM3","CTLA4","LAG3"))))+
+  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=6.4, xmin=6, fill=factor(phen3, levels=c("PD1","TIM3","CTLA4","LAG3"))))+
+  geom_rect(aes(ymax=ymax, ymin=ymin, xmax=6.9, xmin=6.5, fill=factor(phen4, levels=c("PD1","TIM3","CTLA4","LAG3"))))+
+  scale_fill_manual(values = basic.color, na.value="#FFFFFF00", drop=F, limits=c("PD1","TIM3","CTLA4","LAG3"), name="IC")+
+  coord_polar(theta="y") + # Try to remove that to understand how the chart is built initially
+  xlim(c(0, 7))+ # Try to remove that to see how to make a pie chart
+  theme_classic()+
+  guides(fill="none")+
+  theme(strip.background = element_blank(),
+        strip.text = element_text(size=12, face="bold"),
+        axis.line = element_blank(),
+        axis.ticks = element_blank(),
+        # plot.margin = margin(-200,0,0,0),
+        axis.text = element_blank())
+
+mtl_rec<-as.data.frame(matrix(nrow=0, ncol=3))
+colnames(mtl_rec)<-c(colnames(data1)[1:2],"phen")
+for (rec in c("PD1+", "TIM3+", "LAG3+","TIGIT+", "CTLA4+")){
+  temp<-data1[grep(rec, data1$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$phen<-gsub("n","-",data2$phen, fixed = T)
+data2$phen<-gsub("p","+",data2$phen, fixed = T)
+data2$phen<-gsub("_"," ",data2$phen)
+# data2[data2$value < 1, "phen"]<-"Other"
+data2$phen<-gsub("[A-Z]*-*[0-9T]- *", "", 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("PD1", "TIM3", "LAG3","CTLA4")){
+  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$Rec, levels=c("PD1", "TIM3", "CTLA4","LAG3"))
+mtl_rec2$TimePoint<-factor(mtl_rec2$TimePoint, levels=c("W1","W2","W8","W12"))
+mtl_rec2$Population<-factor(mtl_rec2$Population, levels=c("CD8","CD4"))
+
+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 T cells")+
+  guides(color="none")+
+  theme_bw()
+
+ggarrange(g_Rec, g_coex, ncol = 1)
+ggsave(paste0(path,"Analysis/IC_coex.png"), width = 7.5, height = 7.5)

Panel3.R

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+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)