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Scripts de análisis de IC y Panel3

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marcelcosta 2 years ago
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  1. +142
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      IC-coex.R
  2. +130
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      Panel3.R

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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)

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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)

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