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20230821_Revisions.Rmd
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20230821_Revisions.Rmd
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---
title: "R Notebook"
output: html_notebook
---
```{r}
wd <- dirname(getwd())
data_folder <- file.path(wd, "sce_objects", "Revisions")
plot_folder <- file.path(wd,"plots","Revisions")
```
```{r, load and set up data}
all.new <- readRDS("~/data/central_nas/projects/CAF_lung/NSCLC_NEW/sce_objects/final objects with categories/FINAL/FINAL_All_Clustered_FILTERED_inc_tumour_vessel_immune_CAF_Other_new.rds")
fibro.sce <- readRDS("~/data/central_nas/projects/CAF_lung/NSCLC_NEW/sce_objects/final objects with categories/FINAL/FIBRO_CLINICAL-DATA_FILTERED.rds")
all.new$cell_type%>% unique
all.new$hypox_normx <- ifelse(all.new$cell_subtype=="hypoxic" |all.new$cell_type=="hypoxic_CAF"|all.new$cell_type=="hypoxic_tpCAF", "hypoxic","normoxic")
table(all.new$cell_type, all.new$hypox_normx)
table(all.new$hypox_normx)
```
```{r}
#clinical.data <-as.data.frame(colData(immune.sce))
clinical.data <- read.csv(file=file.path(wd,"sce_objects","clinical_data", "clinical_data_ROI_ac_combined_CORRECT.csv"))
clinical.data = clinical.data[!duplicated(clinical.data$Patient_ID),]
clinical.data$X <-NULL
clinical.data$X.1 <-NULL
head(clinical.data)
table(clinical.data$Chemo3)
```
```{r}
clinical.data$Relapse <- ifelse(clinical.data$Relapse ==0, "No Relapse","Relapse")
clinical.data$Chemo3 <- ifelse(clinical.data$Chemo3 ==0, "No Chemo","Chemo")
table(clinical.data$Relapse, clinical.data$Chemo3)
unique(clinical.data$Chemo3)
```
##proportions
## Patients
```{r Calculate proportions hypoxia normoxia patients}
#all.new$Patient_ID <-droplevels(all.new$Patient_ID)
#all.new$Patient_ID <-as.factor(all.new$Patient_ID)
all.new$DX.name[is.na(all.new$DX.name)] <- "NA"
tdat_prop <-as.data.frame(table( all.new[, all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$Patient_ID,
all.new[, all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$hypox_normx))
colnames(tdat_prop)<-c("Patient_ID","Phenotype","n")
tdat_prop <-tdat_prop %>%
dplyr::group_by(Patient_ID) %>%
dplyr::mutate(freq = n / sum(n))
tdat_prop[is.na(tdat_prop)] <- 0
tdat_prop$Phenotype <-droplevels(tdat_prop$Phenotype)
tdat_prop$Patient_ID <-droplevels(tdat_prop$Patient_ID)
tdat_prop_wide <- pivot_wider(tdat_prop,id_cols=c("Patient_ID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_prop_m <-as.matrix(tdat_prop_wide)
rownames(tdat_prop_m)<-tdat_prop_wide$Patient_ID
hc <- hclust(dist(tdat_prop_m[,-1], method="euclidian"),"ward.D2")
tdat_prop_wide$Patient_ID <- factor(tdat_prop_wide$Patient_ID, levels = hc$labels[hc$order])
tdat_prop$Patient_ID <- factor(tdat_prop$Patient_ID, levels = hc$labels[hc$order])
tdat_prop_wide[tdat_prop_wide$normoxic==0,]
colnames(tdat_prop_wide)<-c("Patient_ID","Cells_Hypoxic","Cells_Normoxic")
```
Patient based metaclusters split by metacluster
```{r Plot proportions hypoxia normoxia patients, fig.width=12, fig.height=6}
#Barplot proportions ordered by
p <-ggplot(tdat_prop,aes(x=freq, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")
#plot(p)
##ggsave(plot=p, file=file.path(plot_folder, paste("Barplot_Tcell-Category_over_FILTEREDpatients_ordered-hc.pdf")), width=6, height=6)
#dendrogram coloured by metacluster
ggdend <-hc%>% as.dendrogram() %>% dendextend::set("branches_k_color", k = 4) %>% as.ggdend()
p1 <-ggplot(ggdend, labels = F)+scale_x_continuous(expand = c(0, 0))+
theme_void()+
coord_flip()+
scale_y_reverse()
#n
p2<-ggplot(tdat_prop,aes(x=n, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")+theme(
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())
plt <-p1+
p+theme(legend.position = "none", axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
p2
plot(plt)
##ggsave(plot=plt, file=file.path(plot_folder, paste("Barplot_n_hc_cell_subtype_proportions.pdf")), width=6, height=6)
```
#CAF proportions
## Patients
## Patients are ordered by hierarchical clustering (euclidian, wards.D2)
```{r Calculate proportions CAFs patients}
fibro.sce$DX.name[is.na(fibro.sce$DX.name)] <- "NA"
tdat_fibro_prop <-as.data.frame(table( fibro.sce[, fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$Patient_ID,
fibro.sce[, fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat_fibro_prop)<-c("Patient_ID","Phenotype","n")
tdat_fibro_prop <-tdat_fibro_prop %>%
dplyr::group_by(Patient_ID) %>%
dplyr::mutate(freq = n / sum(n))
tdat_fibro_prop[is.na(tdat_fibro_prop)] <- 0
tdat_fibro_prop$Phenotype <-droplevels(tdat_fibro_prop$Phenotype)
tdat_fibro_prop$Patient_ID <-droplevels(tdat_fibro_prop$Patient_ID)
tdat_fibro_prop_wide <- pivot_wider(tdat_fibro_prop,id_cols=c("Patient_ID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_fibro_prop_m <-as.matrix(tdat_fibro_prop_wide)
rownames(tdat_fibro_prop_m)<-tdat_fibro_prop_wide$Patient_ID
hc <- hclust(dist(tdat_fibro_prop_m[,-1], method="euclidian"),"ward.D2")
tdat_fibro_prop_wide$Patient_ID <- factor(tdat_fibro_prop_wide$Patient_ID, levels = hc$labels[hc$order])
tdat_fibro_prop$Patient_ID <- factor(tdat_fibro_prop$Patient_ID, levels = hc$labels[hc$order])
tdat_fibro_prop_wide
```
Patient based metaclusters split by metacluster
```{r Barplot proportions CAFs patients fig.width=12, fig.height=6}
#Barplot proportions ordered by
mycol <-c("#1D76B5","#EF7D20","#2CA237","#D6272A","#8F67A9","#8D564C","#D278AF","#7F7F7F","#BCBC20","#34B9C9","#AEC6E8")
p <-ggplot(tdat_fibro_prop,aes(x=freq, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+#scale_fill_tableau("Tableau 20")+
scale_fill_manual(values=mycol)
plot(p)
##ggsave(plot=p, file=file.path(plot_folder, paste("Barplot_Tcell-Category_over_FILTEREDpatients_ordered-hc.pdf")), width=6, height=6)
#dendrogram coloured by metacluster
ggdend <-hc%>% as.dendrogram() %>% dendextend::set("branches_k_color", k =4) %>% as.ggdend()
p1 <-ggplot(ggdend, labels = F)+scale_x_continuous(expand = c(0, 0))+
theme_void()+
coord_flip()+
scale_y_reverse()
#n
p2<-ggplot(tdat_fibro_prop,aes(x=n, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+#scale_fill_tableau("Tableau 20")+
theme(
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
scale_fill_manual(values=mycol)
plt <-p1+
p+theme(legend.position = "none", axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
p2
plot(plt)
#ggsave(plot=plt, file=file.path(plot_folder, paste("Barplot_n_hc_CAF_type_proportions_K4.pdf")), width=12, height=6)
```
```{r, message=FALSE}
tdat_fibro_prop_wide
tdat_fibro_prop_wide_cd <- left_join(tdat_fibro_prop_wide, clinical.data,by = "Patient_ID")
tumour.type<-c("Adenocarcinoma","Squamous cell carcinoma")
for(i in unique(fibro.sce$cell_type)){
for(j in unique(tumour.type)){
p <-ggscatter(tdat_fibro_prop_wide_cd[tdat_fibro_prop_wide_cd$DX.name==j,], x = paste(i), y = "Size",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = paste(i,"proportion"), ylab = "Size", title=paste("Pearson",i,j, sep=" "))
plot(p)
p <-ggscatter(tdat_fibro_prop_wide_cd[tdat_fibro_prop_wide_cd$DX.name==j,], x = paste(i), y = "Size",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = paste(i,"proportion"), ylab = "Size", title=paste("Spearman",i,j, sep=" "))
plot(p)
}
}
```
#correlation proportions hypoxia normoxia
```{r Correlate proportions on patient level}
#merge Hypox/Normox CAF density
tdat_prop_fibro_hnx <- left_join(tdat_fibro_prop_wide, tdat_prop_wide)
tdat_prop_fibro_hnx <- left_join(tdat_prop_fibro_hnx, clinical.data.g)
cor(tdat_prop_fibro_hnx[,-1], method="pearson")
#test <- immune.pheno.sub %>% select(contains("Ratio"))
#test$Ratio_tCAF_vCAF <-NULL
sig <- cor.mtest(tdat_prop_fibro_hnx[,-1], conf.level = .95) #is this test right?
#plot
corrplot(cor(tdat_prop_fibro_hnx[,-1], method="pearson"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities",
mar=c(0,0,3,0))
tumour.type <- c("Adenocarcinoma","Squamous cell carcinoma")
method <- c("pearson", "spearman")
for(i in tumour.type){
for(j in method){
tdat <- tdat_prop_fibro_hnx[tdat_prop_fibro_hnx$DX.name==i, !names(tdat_prop_fibro_hnx) %in% colnames(clinical.data.g)]
sig <- cor.mtest(tdat, conf.level = .95) #is this test right?
p <-corrplot(cor(tdat, method=paste(j)),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = paste(j,i, sep=" "),
mar=c(0,0,3,0))
print(p)
}
}
#col_fun = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red"))
tumour.type<-c("Adenocarcinoma","Squamous cell carcinoma")
for(i in unique(fibro.sce$cell_type)){
for(j in unique(tumour.type)){
for(k in method){
p <-ggscatter(tdat_prop_fibro_hnx[tdat_prop_fibro_hnx$DX.name==j,], x = paste(i), y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = k,
xlab = paste(i,"proportion"), ylab = "Cells_Hypoxic", title=paste(k,i,j, sep=" "))
plot(p)
p <-ggscatter(tdat_prop_fibro_hnx[tdat_prop_fibro_hnx$DX.name==j,], x = paste(i), y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = k,
xlab = paste(i,"proportion"), ylab = "Cells_Normoxic", title=paste(k,i,j, sep=" "))
plot(p)
}
}
}
library("ggpubr")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Normoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Normoxia density")
```
## Proprotions on the image level
```{r Calculate normoxia hypoxia on the ROI level}
#all.new$Patient_ID <-droplevels(all.new$Patient_ID)
#all.new$Patient_ID <-as.factor(all.new$Patient_ID)
all.new$DX.name[is.na(all.new$DX.name)] <- "NA"
tdat_prop <-as.data.frame(table( all.new[, all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$RoiID,
all.new[, all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$hypox_normx))
colnames(tdat_prop)<-c("RoiID","Phenotype","n")
tdat_prop <-tdat_prop %>%
dplyr::group_by(RoiID) %>%
dplyr::mutate(freq = n / sum(n))
tdat_prop[is.na(tdat_prop)] <- 0
tdat_prop$Phenotype <-droplevels(tdat_prop$Phenotype)
tdat_prop$RoiID <-droplevels(tdat_prop$RoiID)
tdat_prop_wide <- pivot_wider(tdat_prop,id_cols=c("RoiID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_prop_m <-as.matrix(tdat_prop_wide)
rownames(tdat_prop_m)<-tdat_prop_wide$RoiID
hc <- hclust(dist(tdat_prop_m[,-1], method="euclidian"),"ward.D2")
tdat_prop_wide$RoiID <- factor(tdat_prop_wide$RoiID, levels = hc$labels[hc$order])
tdat_prop$RoiID <- factor(tdat_prop$RoiID, levels = hc$labels[hc$order])
tdat_prop_wide[tdat_prop_wide$normoxic==0,]
colnames(tdat_prop_wide)<-c("RoiID","Cells_Hypoxic","Cells_Normoxic")
```
Patient based metaclusters split by metacluster
```{r Braplot normoxia hypoxia on the ROI level fig.width=12, fig.height=6}
#Barplot proportions ordered by
p <-ggplot(tdat_prop,aes(x=freq, y=RoiID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")
#plot(p)
##ggsave(plot=p, file=file.path(plot_folder, paste("Barplot_Tcell-Category_over_FILTEREDpatients_ordered-hc.pdf")), width=6, height=6)
#dendrogram coloured by metacluster
ggdend <-hc%>% as.dendrogram() %>% dendextend::set("branches_k_color", k = 4) %>% as.ggdend()
p1 <-ggplot(ggdend, labels = F)+scale_x_continuous(expand = c(0, 0))+
theme_void()+
coord_flip()+
scale_y_reverse()
#n
p2<-ggplot(tdat_prop,aes(x=n, y=RoiID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")+theme(
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())
plt <-p1+
p+theme(legend.position = "none", axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
p2
plot(plt)
##ggsave(plot=plt, file=file.path(plot_folder, paste("Barplot_n_hc_cell_subtype_proportions.pdf")), width=6, height=6)
```
#CAF proportions
## Patients
## Patients are ordered by hierarchical clustering (euclidian, wards.D2)
```{r Calculate CAF proportion on the ROI level}
fibro.sce$DX.name[is.na(fibro.sce$DX.name)] <- "NA"
tdat_fibro_prop <-as.data.frame(table( fibro.sce[, fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$RoiID,
fibro.sce[, fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat_fibro_prop)<-c("RoiID","Phenotype","n")
tdat_fibro_prop <-tdat_fibro_prop %>%
dplyr::group_by(RoiID) %>%
dplyr::mutate(freq = n / sum(n))
tdat_fibro_prop[is.na(tdat_fibro_prop)] <- 0
tdat_fibro_prop$Phenotype <-droplevels(tdat_fibro_prop$Phenotype)
tdat_fibro_prop$RoiID <-droplevels(tdat_fibro_prop$RoiID)
tdat_fibro_prop_wide <- pivot_wider(tdat_fibro_prop,id_cols=c("RoiID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_fibro_prop_m <-as.matrix(tdat_fibro_prop_wide)
rownames(tdat_fibro_prop_m)<-tdat_fibro_prop_wide$RoiID
hc <- hclust(dist(tdat_fibro_prop_m[,-1], method="euclidian"),"ward.D2")
tdat_fibro_prop_wide$RoiID <- factor(tdat_fibro_prop_wide$RoiID, levels = hc$labels[hc$order])
tdat_fibro_prop$RoiID <- factor(tdat_fibro_prop$RoiID, levels = hc$labels[hc$order])
tdat_fibro_prop_wide
```
Patient based metaclusters split by metacluster
```{r Barplot CAF proportions on the ROI level, fig.width=12, fig.height=6}
#Barplot proportions ordered by
mycol <-c("#1D76B5","#EF7D20","#2CA237","#D6272A","#8F67A9","#8D564C","#D278AF","#7F7F7F","#BCBC20","#34B9C9","#AEC6E8")
p <-ggplot(tdat_fibro_prop,aes(x=freq, y=RoiID, fill=Phenotype))+geom_bar(stat="identity")+#scale_fill_tableau("Tableau 20")+
scale_fill_manual(values=mycol)
plot(p)
##ggsave(plot=p, file=file.path(plot_folder, paste("Barplot_Tcell-Category_over_FILTEREDpatients_ordered-hc.pdf")), width=6, height=6)
#dendrogram coloured by metacluster
ggdend <-hc%>% as.dendrogram() %>% dendextend::set("branches_k_color", k =4) %>% as.ggdend()
p1 <-ggplot(ggdend, labels = F)+scale_x_continuous(expand = c(0, 0))+
theme_void()+
coord_flip()+
scale_y_reverse()
#n
p2<-ggplot(tdat_fibro_prop,aes(x=n, y=RoiID, fill=Phenotype))+geom_bar(stat="identity")+#scale_fill_tableau("Tableau 20")+
theme(
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
scale_fill_manual(values=mycol)
plt <-p1+
p+theme(legend.position = "none", axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y=element_blank())+
p2
plot(plt)
#ggsave(plot=plt, file=file.path(plot_folder, paste("Barplot_n_hc_CAF_type_proportions_K4.pdf")), width=12, height=6)
```
#correlation proportions
```{r Correlate CAFs and Normoxia Hypoxia on the ROI level}
#merge Hypox/Normox CAF density
tdat_prop_fibro_hnx <- left_join(tdat_fibro_prop_wide, tdat_prop_wide)
cor(tdat_prop_fibro_hnx[,-1], method="pearson")
#test <- immune.pheno.sub %>% select(contains("Ratio"))
#test$Ratio_tCAF_vCAF <-NULL
sig <- cor.mtest(tdat_prop_fibro_hnx[,-1], conf.level = .95) #is this test right?
#plot
corrplot(cor(tdat_prop_fibro_hnx[,-1], method="pearson"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities",
mar=c(0,0,3,0))
#col_fun = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red"))
library("ggpubr")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Normoxia density")
ggscatter(tdat_prop_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Normoxia density")
```
###density
```{r Calculate densities Normoxia hypoxia, message=FALSE, warning=FALSE, echo=FALSE}
tdat_density <-as.data.frame(table(all.new[,all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$RoiID,
all.new[,all.new$Patient_ID!="Control"&
all.new$DX.name=="Adenocarcinoma"|
all.new$DX.name=="Squamous cell carcinoma"]$hypox_normx))
colnames(tdat_density)<-c("RoiID","Phenotype","n")
da <-dplyr::distinct(data.frame("RoiID"=all.new$RoiID,
"Area"=all.new$Area_px_Core,
"Patient_ID"=all.new$Patient_ID,
"DX.name"=all.new$DX.name))
tdat_density <-merge(tdat_density,da,by="RoiID")
tdat_density <-tdat_density[tdat_density$DX.name=="Adenocarcinoma" | tdat_density$DX.name=="Squamous cell carcinoma",]
tdat_density <-tdat_density %>%
#dplyr::count(Type, Area,Patient_ID) %>%
dplyr::group_by(RoiID) %>%
dplyr::mutate(density = n / Area*1000000)
range(tdat_density$density)
tdat_density <-tdat_density %>%
group_by(RoiID,Phenotype) %>%
summarise(across(-c(Patient_ID,n,Area,DX.name), sum, na.rm = TRUE))
range(tdat_density$density)
#t <-merge(t, clinical.data, by="Patient_ID")
#remove super high density outliers (total density >10000)
# tdat_density <-tdat_density[ !tdat_density$Patient_ID%in%surv_excl,]
#CAF type distribution over all patients
tdat_density_wide <- pivot_wider(tdat_density,id_cols=c("RoiID"),names_from = "Phenotype", values_from ="density",names_prefix = "")
tdat_density_m <-as.matrix(tdat_density_wide)
rownames(tdat_density_m)<-tdat_density_wide$RoiID
hc <- hclust(dist(tdat_density_m[,-1]),"ward.D2")
#order Patients after clustering results
tdat_density_wide$RoiID <- factor(tdat_density_wide$RoiID, levels = labels(hc))
tdat_density$RoiID <- factor(tdat_density$RoiID, levels = labels(hc))
#tdat_density_wide <-tdat_density_wide[ !tdat_density_wide$Patient_ID%in%surv_excl,]
colnames(tdat_density_wide)<-c("RoiID","Cells_Hypoxic","Cells_Normoxic")
```
## Barplot showing absolute density per patietn coloured by Fibro category together with hierarchical clustering tree coloured by patient metaclusters.
```{r Barolot CAF densities, fig.width=12, fig.height=6, message=FALSE, warning=FALSE, echo=FALSE}
#barolot (sqrt of density)
p <-ggplot(tdat_density,aes(y=sqrt(density),x=RoiID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")+
theme(legend.position="bottom")#+theme(legend.position = "none")
#barplot total density
p <-ggplot(tdat_density,aes(y=density,x=RoiID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau("Tableau 20")+
theme(legend.position="bottom")#+theme(legend.position = "none")
#coloured dendrogram
ggdend <-hc%>% as.dendrogram() %>% dendextend::set("branches_k_color", k = 8) %>% as.ggdend()
p1 <-ggplot(ggdend, labels = F)+scale_x_continuous(expand = c(0, 0))+
theme_void()
pg <-plot_grid(p1,p, align="v", ncol=1)
pg
##ggsave(plot=pg, file=file.path(plot_folder, paste("Barplot_Tcell-Category-Densities_HC_ordered_dendro.pdf")), width=12, height=6)
```
#correlation Density
```{r Calculate CAF density, message=FALSE, warning=FALSE, echo=FALSE, fig.width=6, fig.height=6}
fibro.sce$NeoAdj <- ifelse(fibro.sce$Chemo==1 |fibro.sce$Radio==1,"NeoAdjuvantTherapy","NoNeoAdjuvantTherapy")
fibro.sce$DX.name[is.na(fibro.sce$DX.name)] <- "NA"
tdat_fibro_density <-as.data.frame(table(fibro.sce[,fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$RoiID,
fibro.sce[,fibro.sce$Patient_ID!="Control"&
fibro.sce$DX.name=="Adenocarcinoma"|
fibro.sce$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat_fibro_density)<-c("RoiID","Phenotype","n")
da <-dplyr::distinct(data.frame("RoiID"=fibro.sce$RoiID,
"Area"=fibro.sce$Area_px_Core,
"Patient_ID"=fibro.sce$Patient_ID,
"DX.name"=fibro.sce$DX.name))
tdat_fibro_density <-merge(tdat_fibro_density,da,by="RoiID")
tdat_fibro_density <-tdat_fibro_density[tdat_fibro_density$DX.name=="Adenocarcinoma" | tdat_fibro_density$DX.name=="Squamous cell carcinoma",]
tdat_fibro_density <-tdat_fibro_density %>%
#dplyr::count(Type, Area,Patient_ID) %>%
dplyr::group_by(RoiID) %>%
dplyr::mutate(density = n / Area*1000000)
range(tdat_fibro_density$density)
tdat_fibro_density <-tdat_fibro_density %>%
group_by(RoiID,Phenotype) %>%
summarise(across(-c(Patient_ID,n,Area,DX.name), sum, na.rm = TRUE))
range(tdat_fibro_density$density)
tdat_fibro_density_wide <- pivot_wider(tdat_fibro_density,id_cols=c("RoiID"),names_from = "Phenotype", values_from ="density",names_prefix = "")
```
```{r Correlations CAF and hypoxic normoxic density, message=FALSE, warning=FALSE, echo=FALSE, fig.width=6, fig.height=6}
#neoadj_roi <- clinical.data %>% filter(NeoAdj == "NeoAdjuvantTherapy") %>% select(RoiID)
#tdat_fibro_density_wide <-tdat_fibro_density_wide[!tdat_fibro_density_wide$RoiID%in% neoadj_roi$RoiID,]
rownames(tdat_fibro_density_wide)<-tdat_fibro_density_wide$RoiID
library(corrplot)
cor(tdat_fibro_density_wide[,-1], method="pearson")
#corrplot(cor(tdat_fibro_density_wide[,-1], method="pearson"), col=rev(brewer.pal(10,"RdBu")))
#test <- immune.pheno.sub %>% select(contains("Ratio"))
#test$Ratio_tCAF_vCAF <-NULL
sig <- cor.mtest(tdat_fibro_density_wide[,-1], conf.level = .95) #is this test right?
#plot
corrplot(cor(tdat_fibro_density_wide[,-1], method="pearson"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities",
mar=c(0,0,3,0))
#col_fun = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red"))
#pdf(file=file.path(plot_folder, "CAF_density_corrplot_upper.pdf"), width=6, height=6)
corrplot(cor(tdat_fibro_density_wide[,-1], method="pearson"),
type="upper",
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
#col=rev(brewer.pal(8,"RdBu")),
col=rev(COL2('RdBu', 8)),
method="square",
pch.col = "white",
#type="upper",
# title = "Correlation of CAF type densities",
mar=c(0,0,3,0))
#dev.off()
#col=wes_palette("Zissou1", 100, type = "continuous"),
#merge Hypox/Normox CAF density
tdat_fibro_hnx <- left_join(tdat_fibro_density_wide, tdat_density_wide)
cor(tdat_fibro_hnx[,-1], method="pearson")
#test <- immune.pheno.sub %>% select(contains("Ratio"))
#test$Ratio_tCAF_vCAF <-NULL
sig <- cor.mtest(tdat_fibro_hnx[,-1], conf.level = .95) #is this test right?
#plot
corrplot(cor(tdat_fibro_hnx[,-1], method="pearson"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities",
mar=c(0,0,3,0))
#col_fun = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red"))
library("ggpubr")
ggscatter(tdat_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_fibro_hnx, x = "iCAF", y = "Cells_Hypoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Hypoxia density")
ggscatter(tdat_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Normoxia density")
ggscatter(tdat_fibro_hnx, x = "iCAF", y = "Cells_Normoxic",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Normoxia density")
```
```{r Correlations all cells and hypoxic normoxic patch fractions, message=FALSE, warning=FALSE, echo=FALSE, fig.width=6, fig.height=6}
#calculate density of all cells
all.hypox$NeoAdj <- ifelse(all.hypox$Chemo==1 |all.hypox$Radio==1,"NeoAdjuvantTherapy","NoNeoAdjuvantTherapy")
all.hypox$DX.name[is.na(all.hypox$DX.name)] <- "NA"
tdat_all_density <-as.data.frame(table(all.hypox[,all.hypox$Patient_ID!="Control"&
all.hypox$DX.name=="Adenocarcinoma"|
all.hypox$DX.name=="Squamous cell carcinoma"]$RoiID,
all.hypox[,all.hypox$Patient_ID!="Control"&
all.hypox$DX.name=="Adenocarcinoma"|
all.hypox$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat_all_density)<-c("RoiID","Phenotype","n")
da <-dplyr::distinct(data.frame("RoiID"=all.hypox$RoiID,
"Area"=all.hypox$Area_px_Core,
"Patient_ID"=all.hypox$Patient_ID,
"DX.name"=all.hypox$DX.name))
tdat_all_density <-merge(tdat_all_density,da,by="RoiID")
tdat_all_density <-tdat_all_density[tdat_all_density$DX.name=="Adenocarcinoma" | tdat_all_density$DX.name=="Squamous cell carcinoma",]
tdat_all_density <-tdat_all_density %>%
#dplyr::count(Type, Area,Patient_ID) %>%
dplyr::group_by(RoiID) %>%
dplyr::mutate(density = n / Area*1000000)
range(tdat_all_density$density)
tdat_all_density <-tdat_all_density %>%
group_by(RoiID,Phenotype) %>%
summarise(across(-c(Patient_ID,n,Area,DX.name), sum, na.rm = TRUE))
range(tdat_all_density$density)
tdat_all_density_wide <- pivot_wider(tdat_all_density,id_cols=c("RoiID"),names_from = "Phenotype", values_from ="density",names_prefix = "")
#neoadj_roi <- clinical.data %>% filter(NeoAdj == "NeoAdjuvantTherapy") %>% select(RoiID)
#tdat_all_density_wide <-tdat_all_density_wide[!tdat_all_density_wide$RoiID%in% neoadj_roi$RoiID,]
rownames(tdat_all_density_wide)<-tdat_all_density_wide$RoiID
library(corrplot)
cor(tdat_all_density_wide[,-1], method="pearson")
#plot pearson
sig <- cor.mtest(tdat_all_density_wide[,-1], conf.level = .95,method="pearson")
pdf(file=file.path(plot_folder, "All_cell_density_corrplot_pearson.pdf"), width=6, height=6)
corrplot(cor(tdat_all_density_wide[,-1], method="pearson"),
type="upper",
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
#col=rev(brewer.pal(8,"RdBu")),
col=rev(COL2('RdBu', 8)),
method="square",
pch.col = "white",
#type="upper",
title = "Correlation of all celltype densities",
mar=c(0,0,3,0))
dev.off()
#plot spearman
sig <- cor.mtest(tdat_all_density_wide[,-1], conf.level = .95, method="spearman")
#plot
pdf(file=file.path(plot_folder, "All_cell_density_corrplot_spearman.pdf"), width=6, height=6)
corrplot(cor(tdat_all_density_wide[,-1], method="spearman"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of all celltype densities, spearman",
mar=c(0,0,3,0))
dev.off()
#merge Hypox/Normox CAF density
tdat_density <- table(all.hypox$hypox_normx, all.hypox$RoiID) %>% prop.table(margin=2) %>%as.data.frame()
tdat_density_wide <- pivot_wider(tdat_density, id_cols="Var2", names_from="Var1", values_from=Freq)
colnames(tdat_density_wide) <- c("RoiID","hypoxic_patch","normoxic_patch")
tdat_density_wide %>% head
tdat_all_hnx <- left_join(tdat_all_density_wide, tdat_density_wide)
tdat_all_hnx%>% head()
cor(tdat_all_hnx[,-1], method="pearson")
#plot pearson PATCH
sig <- cor.mtest(tdat_all_hnx[,-1], conf.level = .95,method="pearson")
pdf(file=file.path(plot_folder, "All_cell_density_patches_corrplot_pearson.pdf"), width=6, height=6)
corrplot(cor(tdat_all_hnx[,-1], method="pearson"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities, patch fraction, pearson",
mar=c(0,0,3,0))
dev.off()
#plot spearman PATCH
sig <- cor.mtest(tdat_all_hnx[,-1], conf.level = .95, method="spearman")
pdf(file=file.path(plot_folder, "All_cell_density_patches_corrplot_spearman.pdf"), width=6, height=6)
corrplot(cor(tdat_all_hnx[,-1], method="spearman"),
tl.cex = .7,
number.cex = .7,
order = "hclust",
p.mat = sig$p,
insig = "label_sig",
tl.col = "black",
addrect = 3,
tl.srt = 90,
sig.level = c(.001, .01, .05),
pch.cex = .9,
col=rev(brewer.pal(10,"RdBu")),
pch.col = "white",
#type="upper",
title = "Correlation of CAF type densities, patch fraction, spearman",
mar=c(0,0,3,0))
dev.off()
library("ggpubr")
pdf(file=file.path(plot_folder, "iCAF_hypoxicPATCH_pearson-correlation.pdf"), width=6, height=6)
ggscatter(tdat_all_hnx, x = "iCAF", y = "hypoxic_patch",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Fraction hypoxic patch")
dev.off()
pdf(file=file.path(plot_folder, "iCAF_hypoxicPATCH_spearman-correlation.pdf"), width=6, height=6)
ggscatter(tdat_all_hnx, x = "iCAF", y = "hypoxic_patch",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Fraction hypoxic patch")
dev.off()
pdf(file=file.path(plot_folder, "iCAF_normoxicPATCH_pearson-correlation.pdf"), width=6, height=6)
ggscatter(tdat_all_hnx, x = "iCAF", y = "normoxic_patch",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "iCAF density", ylab = "Fraction normoxic patch")
dev.off()
pdf(file=file.path(plot_folder, "iCAF_normoxicPATCH_spearman-correlation.pdf"), width=6, height=6)
ggscatter(tdat_all_hnx, x = "iCAF", y = "normoxic_patch",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "iCAF density", ylab = "Fraction normoxic patch")
dev.off()
```
```{r Hypoxic and notmoxic defined cells masks, fig.width=50, fig.height=50}
library(cytomapper)
path.to.images <- file.path("~/data/central_nas/projects/CAF_lung/NSCLC_NEW/cells_mask_87A")
all_masks <- loadImages(path.to.images, pattern = "2020121_LC_NSCLC_TMA_87_A")
mcols(all_masks)$ImageNb <- c("1" ,"10","100","102", "104","105","106","107","108","109", "11","110","111","112","114","115","116","117","118","119","12","121","122" ,"123", "124" ,"125", "126", "127", "128" ,"129" ,"13" , "130" ,"131" ,"132" ,"133" ,"134" ,"135" ,"137", "138", "14" , "15" , "16" , "17" , "18" , "19" ,"2" ,"20" ,"21" ,"22" ,"23" , "24" , "25" , "26" , "27" , "28" ,"29" , "3" , "30" ,"31" ,"32" ,"33" ,"34" ,"36", "37" , "38" , "39" , "4" , "40" ,"41" , "42" , "43" ,"44" ,"45" ,"46" ,"47" ,"48" , "5" , "50" , "51" , "52" , "53", "54" ,"55" ,"56" , "57" ,"58" , "59" , "6" ,"60" , "61" , "63" , "64" ,"65" , "66" , "67" ,"68" , "69" , "7" , "70" , "71", "72" , "73" ,"74" ,"75" , "76" , "77", "78" , "79" , "8" , "80" ,"81" ,"82" ,"83" , "85", "86" , "87" ,"88" , "89" , "9" ,"90" ,"91" , "92" , "93" ,"94" ,"95" ,"96" , "97" ,"98" ,"99" )
#mcols(all_masks)$ImageNb <- c(ac_sub$ImageNb)
head(unique(as.numeric(all_masks[[1]])))
all_masks <- scaleImages(all_masks, 2^16-1)
tma87 <- all.new[, all.new$TMA=="87A"]
tma87 <- all.pat.roi[, all.pat.roi$TMA=="87A"]
tma87 <- all.sce[, all.sce$TMA=="87A"]
tma87$ImageNb <- tma87$acID
#pdf(file=file.path("/mnt/lena_processed2/NSCLC_NEW/plots_cytomapper","panCK_SMA expression_87A_allCells_Categorised.pdf"), width=20, height=20)
p1<-plotCells(all_masks, object = tma87,
img_id = "ImageNb", cell_id = "CellNumber",
colour_by = c("Pan Cytokeratin + Keratin Epithelial","SMA"),
exprs_values = "c_counts_asinh_scaled", return_plot = TRUE)
#dev.off()
#pdf(file=file.path("/mnt/lena_processed2/NSCLC_NEW/plots_cytomapper","87A_allCells_Categorised.pdf"), width=20, height=20)
p2 <-plotCells(all_masks, object = tma87,
img_id = "ImageNb", cell_id = "CellNumber",
colour_by = "cell_category",
colour = list(cell_category = c("Tumour"="red","Immune"="blue","T cell"="blue","vessel"="yellow", "Fibroblast"="green")), return_plot = TRUE)
#dev.off()
#pdf(file=file.path("/mnt/lena_processed2/NSCLC_NEW/plots_cytomapper","87A_tumour_stroma_Masks.pdf"), width=20, height=20)
p3 <-plotCells(all_masks, object = tma87,
img_id = "ImageNb", cell_id = "CellNumber",
colour_by = "Mask",
colour = list(Mask = c("tumour"="red","stroma"="green")), return_plot = TRUE)
#dev.off()
pdf(file=file.path("~/data/central_nas/projects/CAF_lung/RAW/NSCLC_TMA/ImcSegmentationPipeline/analysis/target_tiffs/plots","87A_allCells_Categorised_hypoxic-normoxic.pdf"), width=20, height=20)
p3 <-plotCells(all_masks, object = tma87,
img_id = "ImageNb", cell_id = "CellNumber",
colour_by = "hypox_normx",
colour = list(hypox_normx = c("hypoxic"="red","normoxic"="green")), return_plot = TRUE)
dev.off()
```
```{r Patch detection normo, fig.width=30, fig.height=10}
tma87 <- all.hypox[, all.hypox$TMA=="87A"]
test.sce <- tma87[, tma87$acID==71|tma87$acID==72 | tma87$acID==73]
test.sce <- buildSpatialGraph(test.sce, img_id = "ImageNumber",
type = "expansion",
threshold = 15,
coords=c("Center_X","Center_Y"))
pdf(file=file.path(plot_folder, "Hypox_normox_cells_graph_noedges.pdf"), width=30, height=10)
plotSpatial(test.sce,
img_id = "ImageNumber",
node_color_by = "cell_category",
#node_shape_by = "ImageNumber",
node_size_by = "Area",
draw_edges = F,
colPairName = "expansion_interaction_graph",
directed = F,
scales = "free",
coords=c("Center_X","Center_Y"),
ncols=3,
edge_color_fix="grey")
dev.off()
plotSpatial(test.sce,
img_id = "ImageNumber",
node_color_by = "hypox_normx",
node_shape_by = "ImageNumber",
node_size_by = "Area",
draw_edges = F,
colPairName = "expansion_interaction_graph",
directed = FALSE,
scales = "free",
coords=c("Center_X","Center_Y"))
pdf(file=file.path(plot_folder, "Hypox_normox_cells_graph_CAIX.pdf"), width=30, height=10)
plotSpatial(test.sce, img_id = "ImageNumber",
node_color_by = "Carbonic Anhydrase IX",
assay_type = "c_counts_asinh_scaled",
#node_shape_by = "ImageNumber",
node_size_by = "Area",
draw_edges = F,
colPairName = "expansion_interaction_graph",
edge_color_by = "hypox_normx",
coords=c("Center_X","Center_Y"),
ncol=3)
dev.off()
test.sce <- patchDetection(test.sce,
patch_cells = test.sce$cell_category == "Immune",
colPairName = "expansion_interaction_graph",
expand_by = 5,
img_id = "ImageNumber",
coords=c("Center_X","Center_Y"),
min_patch_size=10)