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Analysis_Tcells.Rmd
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---
title: "R Notebook - Analysis T cells"
output:
html_document:
df_print: paged
---
```{r, import libraries, echo=F, warnings=F, message=F}
library(igraph)
library(SingleCellExperiment)
library(S4Vectors)
library(stringr)
library(DT)
library(dplyr)
library(tidyr)
library(mclust)
library(ggplot2)
library(RColorBrewer)
library(scater)
library(Rphenoannoy)
library(factoextra)
library(cluster)
library(dendextend)
library(ggthemes)
library(ggpubr)
library(dplyr)
#library(tidyverse)
library(RColorBrewer)
library(pals)
library(qwraps2)
library(table1)
library(SingleCellExperiment)
library(tidyr)
library(scater)
library(ggridges)
library(viridis)
library(viridisLite)
library(ggplot2)
library(data.table)
library(CATALYST)
library(gridExtra)
library(Rphenograph)
library(ComplexHeatmap)
library(CATALYST)
library(scales)
library(survival)
library(broom)
library(pheatmap)
library(FlowSOM)
library(Seurat)
library(Rphenoannoy)
library(dplyr)
library(data.table)
library(ggthemes)
library(diffcyt)
library(edgeR)
library(rstatix)
library(dendextend)
library(ggdendro)
library(dendextend)
library(FactoMineR)
library(factoextra)
library(survminer)
library(corrplot)
library(rstatix)
library(graphics)
library(cowplot)
library(cluster)
library(glmnet)
library(fastDummies)
library(ggsci)
library(FactoMineR)
library(factoextra)
set.seed(101100)
```
```{r, Set wd and load data, echo=F, warnings=F, message=F}
#set working directory
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","immune","Tcell"))
#RAW
#tcell.sce <- readRDS(file=file.path(data_folder, "FINAL_Tcells-minusImmune_workingfile.rds"))
#tcell.sce <- readRDS(file=file.path(data_folder, "FINAL_Analysis_Tcell-clinicaldata_area_RAW.rds"))
#workingfile
#saveRDS(tcell.sce, file=file.path(data_folder, "FINAL_Analysis_Tcell-clinicaldata_area_RAW.rds"))
#saveRDS(tcell.sce, file=file.path(data_folder, "FINAL_Analysis_Tcell-clinicaldata_workingfile.rds"))
#clinical.data
data_folder <-(file.path(wd,"sce_objects","final objects with categories","FINAL"))
tcell.sce <- readRDS(file=file.path(data_folder, "TCELL-only_CLINICAL-DATA_FILTERED.rds"))
tcell.sce$DX.name[is.na(tcell.sce$DX.name)]<-"NA"
```
```{r, load clinical data, echo=F, warnings=F, message=F}
clinical.data <- read.csv(file=file.path(wd,"sce_objects","clinical_data", "clinical_data_ROI_ac_combined_CORRECT.csv"))
clinical.data$TMA <-clinical.data$TMA.x
clinical.data$TMA.x <-NULL
clinical.data$TMA.y <-NULL
clinical.data$X.1 <-NULL
clinical.data$X <-NULL
head(clinical.data)
unique(clinical.data$DX.name)
clinical.data$DX.name[clinical.data$Patient_ID=="Control"] <-"Control"
table(clinical.data$DX.name)
clinical.data$Patient_ID %>% unique() %>% length() #1071 PATIENTS IN TOTAL
area <- read.csv(file=file.path(wd,"clinical_data", "area.csv"))
area$X <- NULL
area$Tma_ac <- area$TMA_ImageID
area$TMA_ImageID <-NULL
clinical.data = clinical.data[!duplicated(clinical.data$Patient_ID),]
```
Table with Immune cell numbers including T cells removing undefined
```{r, Table with immune cell numbers per patient, echo=FALSE, message=FALSE, warning=FALSE}
#All T cells together
tbl <- as.data.frame(table(tcell.sce[,tcell.sce$Patient_ID!="Control" ]$Patient_ID))
colnames(tbl) <-c("Patient ID", "T cell number overall")
#write.csv(tbl, file=file.path(plot_folder, paste("Immune cell numbers per patient overall including T-excluding undefined cells.csv")))
#print(tbl)
#summary(tbl$`T cell number overall`) #3-6259
#tbl[tbl$`T cell number overall` <=100,] #31 patients have less than 100 T cells=3% but 10% of median/ 89 patients have less than 300 T cells
#Lowest 10% of all patients' T cell numbers
#tbl[order(tbl$`T cell number overall`),]$`T cell number overall`[1:100] #1-23
#Highest 10% of all patients' T cell numbers
#tbl[order(-tbl$`T cell number overall`),]$`T cell number overall`[1:100] #760-2378
#tbl[tbl$`T cell number overall` <=15,] #102 patients have less than 100 T cells=10%. 77 patients have less than 15 T cells
all_tcell_pat <- tbl[tbl$`T cell number overall` <=20,]$`Patient ID`
length(unique(tcell.sce$Patient_ID))
```
Remove ROIs
```{r, Table with Immune cell numbers per image, echo=FALSE, message=FALSE, warning=FALSE}
#All Immune cells together
tbl <- as.data.frame(table(tcell.sce[,tcell.sce$Patient_ID!="Control"]$RoiID))
colnames(tbl) <-c("ROI ID", "Immune cell number overall")
#write.csv(tbl, file=file.path(plot_folder, paste("Immune cell numbers per ROI overall including T cells-excluding undefined.csv")))
#print(tbl)
#summary(tbl$`Immune cell number overall`) #1-5075
#tbl[tbl$`Immune cell number overall` <=10,] #93 images have less than 50 Immune cells (10% of median)
#Lowest 10% of all patients' Immune cell numbers
#tbl[order(tbl$`Immune cell number overall`),]$`Immune cell number overall`[1:200] #1-110
#Highest 10% of all patients' Immune cell numbers
#tbl[order(-tbl$`Immune cell number overall`),]$`Immune cell number overall`[1:200] #1385-5075
#per image cut at 50 Immune cells per image equals lowest 5% -> ensures that there's at least 50 Immune cells per patient
all_tcell_roi <- tbl[tbl$`Immune cell number overall` <=10,]$`ROI ID`
```
Remove patients and roi
```{r, patient roi and pat_roi removal, echo=FALSE, message=FALSE, warning=FALSE}
#Patient removal
tcell.sce_pat <- tcell.sce[,tcell.sce$Patient_ID!="Control"&
!tcell.sce$Patient_ID%in%all_tcell_pat]
length(unique(tcell.sce_pat$Patient_ID)) #954
#Roi removal
tcell.sce_roi <- tcell.sce[,tcell.sce$Patient_ID!="Control"&
!tcell.sce$RoiID%in%all_tcell_roi]
length(unique(tcell.sce_roi$Patient_ID)) #987
#Patient & Roi removal
tcell.sce_pat.roi <- tcell.sce[,tcell.sce$Patient_ID!="Control"&
!tcell.sce$Patient_ID%in%all_tcell_pat&
!tcell.sce$RoiID%in%all_tcell_roi]
length(unique(tcell.sce_pat.roi$Patient_ID)) #954
```
```{r, Save SCE patient and roi removed, include=FALSE, echo=FALSE,warning=FALSE, message=FALSE, eval=FALSE}
data_folder <-(file.path(wd,"sce_objects","immune","Tcell"))
#saveRDS(tcell.sce_pat.roi,file=file.path(data_folder, paste("Tcell_sce_pat_roi_rem.rds",sep="")))
#saveRDS(tcell.sce_roi,file=file.path(data_folder, paste("Tcell_sce_roi_rem.rds",sep="")))
#saveRDS(tcell.sce_pat,file=file.path(data_folder, paste("Tcell_sce_pat_rem.rds",sep="")))
tcell.sce_pat.roi <-readRDS(file=file.path(data_folder, paste("Tcell_sce_pat_roi_rem.rds",sep="")))
tcell.sce_roi <-readRDS(file=file.path(data_folder, paste("Tcell_sce_roi_rem.rds",sep="")))
tcell.sce_pat <-readRDS(file=file.path(data_folder, paste("Tcell_sce_pat_rem.rds",sep="")))
```
##Analysis
#Proportions T cell CATEGORY
## **Analysis**
In the following, only tumours that were identified as adenocarcinomas or squamous cell carcinomas by pathologists will be used.
## **T cell Category**
## **Proportions**
Optimal number of patient metaclusters T cell category proportions
```{r, optimal number of clusters for non T cell category type proportion clusters, message=FALSE, warning=FALSE, echo=FALSE, out.width="50%", fig.width=6, fig.height=4}
#Calculate Proportions
neoadj_pat <- clinical.data %>% filter(NeoAdj == "NeoAdjuvantTherapy") %>% select(Patient_ID)
tdat <-as.data.frame(table( tcell.sce_pat[, tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$Patient_ID,
tcell.sce_pat[, tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat)<-c("Patient_ID","Phenotype","n")
tdat <-tdat %>%
dplyr::group_by(Patient_ID) %>%
dplyr::mutate(freq = n / sum(n))
tdat[is.na(tdat)] <- 0
tdat$Phenotype <-droplevels(tdat$Phenotype)
tdat$Patient_ID <-droplevels(tdat$Patient_ID)
tdat_wide <- pivot_wider(tdat,id_cols=c("Patient_ID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_wide <-tdat_wide[!tdat_wide$Patient_ID%in% neoadj_pat$Patient_ID,]
tdat_m <-as.matrix(tdat_wide)
rownames(tdat_m)<-tdat_wide$Patient_ID
#tdat_m <-tdat_m[,1:2]
hc <- hclust(dist(tdat_m[,-1], method="euclidian"),"ward.D2")
#devtools::install_github("kassambara/factoextra")
sbg <-cutree(hc, k=2)
fviz_cluster(list(data = tdat_wide[,-1], cluster = sbg))
fviz_nbclust(tdat_wide[,-1], FUN = hcut, method = "wss", k.max=15)
fviz_nbclust(tdat_wide[,-1], FUN = hcut, method = "gap_stat", k.max=15)
fviz_nbclust(tdat_wide[,-1], FUN = hcut, method = "silhouette", k.max=15)
#install.packages("cluster")
gap_stat <- clusGap(tdat_wide[,-1], FUN = hcut, nstart = 25, K.max = 10, B = 100)
fviz_gap_stat(gap_stat)
# Compute distance matrix
#res.dist <- dist(tdat_wide[,-1], method = "euclidean")
```
## Patients are ordered by hierarchical clustering (euclidian, wards.D2)
```{r, T cell category proportions ordered by hierarchical clustering, message=FALSE, warning=FALSE, echo=FALSE}
tdat <-as.data.frame(table( tcell.sce_pat[, tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$Patient_ID,
tcell.sce_pat[, tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat)<-c("Patient_ID","Phenotype","n")
tdat <-tdat %>%
dplyr::group_by(Patient_ID) %>%
dplyr::mutate(freq = n / sum(n))
tdat[is.na(tdat)] <- 0
tdat$Phenotype <-droplevels(tdat$Phenotype)
tdat$Patient_ID <-droplevels(tdat$Patient_ID)
tdat_wide <- pivot_wider(tdat,id_cols=c("Patient_ID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_m <-as.matrix(tdat_wide)
rownames(tdat_m)<-tdat_wide$Patient_ID
hc <- hclust(dist(tdat_m[,-1], method="euclidian"),"ward.D2")
tdat_wide$Patient_ID <- factor(tdat_wide$Patient_ID, levels = hc$labels[hc$order])
tdat$Patient_ID <- factor(tdat$Patient_ID, levels = hc$labels[hc$order])
tdat_wide
```
Patient based metaclusters split by metacluster
```{r, Barplot T cell category proportions, message=FALSE, warning=FALSE, echo=FALSE, fig.width=12, fig.height=6}
#Barplot proportions ordered by
p <-ggplot(tdat,aes(x=freq, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau()
#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 = 3) %>% 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,aes(x=n, y=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau()+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_type_proportions.pdf")), width=6, height=6)
```
```{r, merge hc metacluster into t_dat T cell category proportions, message=FALSE, warning=FALSE, echo=FALSE, fig.width=12, fig.height=6}
clinical.data = clinical.data[!duplicated(clinical.data$Patient_ID),]
#cut dendrogram, add metacluster info to tdat
ct <-as.data.frame(cutree(hc, k= 3))
colnames(ct)<-"metacluster"
ct$metacluster <-as.factor(ct$metacluster)
ct$Patient_ID <- rownames(ct)
tdat_wide_ct <-left_join(tdat_wide, ct, by="Patient_ID")
tdat_wide_ct <- left_join(tdat_wide_ct, clinical.data, by="Patient_ID")
table(tdat_wide_ct$metacluster)
#split barlot by metacluster
tdat_ct <-merge(tdat, ct, by="Patient_ID")
p <-ggplot(tdat_ct,aes(y=freq,x=Patient_ID, fill=Phenotype))+geom_bar(stat="identity")+scale_fill_tableau()+
theme(legend.position="bottom")+
facet_grid(~ metacluster, scales = "free", space = "free")
#ggsave(plot=p, file=file.path(plot_folder, paste("Barplot_split_metacluster_tcell-category_proportions.pdf")))
plot(p)
```
## **Survival analysis** - T cell category
T cell categories (CD4 and CD8) are grouped into "high" and "low" using the proportion's median as a cut-off.
- high > median
- low < median
Patients who received neoadjuvant treatment are excluded from survival analysis.
```{r, survival data T cell category proportions high-low, message=FALSE, warning=FALSE, echo=FALSE}
neoadj_pat <- clinical.data %>% filter(NeoAdj == "NeoAdjuvantTherapy") %>% select(Patient_ID)
surv_dat <-as.data.table(tdat_wide_ct)
surv_dat<- surv_dat[is.na(surv_dat$OS) !=T,]
# Censoring for OS (1 = event (death due to disease), 0 = censored)
surv_dat[,censoringOS := 0]
surv_dat[Ev.O == 1,censoringOS := 1]
# Censoring for disease free survival (1 = event (recurrence), 0 = censored)
surv_dat[ ,censoringDFS := 0]
surv_dat[ OS > DFS, censoringDFS := 1]
surv_dat$censoringDFS <-as.numeric(as.character(surv_dat$censoringDFS))
surv_dat <-surv_dat[surv_dat$DX.name=="Adenocarcinoma"|surv_dat$DX.name=="Squamous cell carcinoma",]
surv_dat <-surv_dat[!surv_dat$Patient_ID%in% neoadj_pat,]
#high low by median
surv_dat$CD4_G <- ifelse(surv_dat$CD4 >summary(surv_dat$CD4)[3],"CD4 high","CD4 low")
surv_dat$CD8_G <- ifelse(surv_dat$CD8 >summary(surv_dat$CD8)[3],"CD8 high","CD8 low")
surv <-c("CD8_G","CD4_G", "metacluster")
```
#CoxPH for T cell category proportions corrected for Stage, Grade and M
```{r, Coxph T cell category proportions corrected, echo=F, message=FALSE, warning=FALSE}
surv_dat
surv_dat<-surv_dat[!surv_dat$OS == 0, ]
time <- surv_dat$OS
status <- surv_dat$censoringOS
surv_dat$metacluster <-as.factor(surv_dat$metacluster)
surv_dat$Grade <-as.numeric(surv_dat$Grade)
rownames(surv_dat) <- surv_dat$Patient_ID
res.cox <- coxph(Surv(time, status) ~ metacluster, data = surv_dat)
res.cox
res.cox <- coxph(Surv(time, status) ~ CD4 + CD8 + Stage+Grade+M.new, data = surv_dat)
#res.cox <- coxph(Surv(time, status) ~ `CD4+`+tpCAF+iCAF+mCAF+dCAF+vCAF+`SMA+ CAF`+`IDO+ CAF`+`dividing tpCAF`+`hypoxic CAF`+Grade, data = surv_dat)
summary(res.cox)
td_res = tidy(res.cox, exponentiate = TRUE)
td_res = td_res %>% arrange(estimate) %>% mutate(term = factor(term,levels = term))
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.high = estimate + std.error)
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.low = estimate - std.error)
td_res<-td_res[!td_res$std.error == 0, ]
td_res <-td_res[td_res$term !="Grade"&
td_res$term !="Stage"&
td_res$term !="M.new",]
p <-ggplot(td_res, aes(x = term, y = estimate,color =p.value < 0.05)) +
geom_point(size = 4) +
geom_errorbar(aes(ymax = conf.high, ymin = conf.low))+
scale_colour_viridis_d("p value < 0.05")+
coord_flip()+
#xlim(0,2)+
geom_hline(yintercept = 1,color = 'black')+
theme_bw()+
scale_colour_tableau()
#
plot(p)
```
## Not split by tumour type (AC and SQC together)
```{r, survival T cell category proportions not split high low, message=FALSE, warning=FALSE, echo=FALSE,out.width="50%", fig.height=4, fig.width=6}
#not split by Tumour Type (AC/SQC)
for (i in surv){
#for (k in unique(surv_dat$DX.name)){
#dat <-surv_dat[surv_dat$DX.name==k,]
dat <-surv_dat
k="AC & SCC together"
#OS
pw<-pairwise_survdiff(Surv(OS, censoringOS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(OS, censoringOS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = F,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,5000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("OS over",i,"per",k))
# facet_grid(.~surv_dat[[i]])
print(p)
#print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
facet_wrap(~strata)+theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#km_trt_fit
#print(p)
#disease free survival
pw<-pairwise_survdiff(Surv(DFS, censoringDFS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(DFS, censoringDFS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = T,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,5000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("DFS over",i,"per",k))#+
# facet_grid(.~surv_dat[[i]])
print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#print(p)
#km_trt_fit
}
```
## Split by tumour type (AC and SQC individually)
```{r, survival T cell category proportions split high low, message=FALSE, warning=FALSE, echo=FALSE,out.width="50%", fig.height=4, fig.width=6}
#OS survival
surv <-c("CD8_G","CD4_G", "metacluster")
#split by Tumour Type (AC/SQC)
for (i in surv){
for (k in unique(surv_dat$DX.name)){
dat <-surv_dat[surv_dat$DX.name==k,]
dat$CD4_G <- ifelse(dat$CD4 >summary(dat$CD4)[3],"CD4 high","CD4 low")
dat$CD8_G <- ifelse(dat$CD8 >summary(dat$CD8)[3],"CD8 high","CD8 low")
#OS
pw<-pairwise_survdiff(Surv(OS, censoringOS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
#abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(OS, censoringOS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = F,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,2000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("OS over",i,"per",k))
# facet_grid(.~surv_dat[[i]])
print(p)
#print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#km_trt_fit
#print(p)
#disease free survival
pw<-pairwise_survdiff(Surv(DFS, censoringDFS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(DFS, censoringDFS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = T,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,2000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("DFS over",i,"per",k))#+
# facet_grid(.~surv_dat[[i]])
print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#print(p)
#km_trt_fit
}
}
```
## Lasso-regressed cox-ph model
Including clinical parameters, patient metacluster and T cell categories (CD4 vs CD8) classified as high / low as well as the proportion (continuous).
```{r, coxph T cell category proportions, message=FALSE, warning=FALSE, echo=FALSE,out.width="50%", fig.width=6, fig.height=6}
surv_dat$metacluster <-as.numeric(surv_dat$metacluster)
fd_test <- fastDummies::dummy_cols(surv_dat, select_columns = c("CD4_G","CD8_G","Typ","Grade","T.new","N","M.new","Stage","Smok","Gender", "metacluster")) #
fd_test<-fd_test[!fd_test$OS == 0, ]
time <- fd_test$OS
status <- fd_test$censoringOS
rownames(fd_test) <- fd_test$Patient_ID
fd_test <-fd_test %>%select(-c("CD4_G","CD8_G","metacluster",colnames(clinical.data),contains("censor"))) #))
fd_test[is.na(fd_test)] <- 0
fd_test_matrix <-data.matrix(fd_test)
#determine lambda
cv.fit <- cv.glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
fit <- glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
#plot(cv.fit)
#cv.fit$lambda.min
#loop to determine optimal lambda for lasso regression
MSEs <- NULL
for (i in 1:100){
cv <- cv.glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
MSEs <- cbind(MSEs, cv$cvm)
}
rownames(MSEs) <- cv$lambda
lambda.min <- as.numeric(names(which.min(rowMeans(MSEs))))
#coef(fit, s = lambda.min)
Coefficients <- coef(fit, s = lambda.min)
Active.Index <- which(Coefficients != 0)
Active.Coefficients <- Coefficients[Active.Index]
#Coefficients
active_coefficients <- Coefficients[,1] != 0
x <- fd_test_matrix[,active_coefficients]
x <- data.frame(x)
vct <-colnames(x)
coxdf<-coxph(Surv(time,status)~.,data=x)
td_res = tidy(coxdf, exponentiate = TRUE)
td_res = td_res %>% arrange(estimate) %>% mutate(term = factor(term,levels = term))
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.high = estimate + std.error)
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.low = estimate - std.error)
td_res<-td_res[!td_res$std.error == 0, ]
na_td_res <- td_res[-grep("NA",td_res$term),]
p <-ggplot(na_td_res, aes(x = term, y = estimate,color =p.value < 0.05)) +
geom_point(size = 4) +
geom_errorbar(aes(ymax = conf.high, ymin = conf.low))+
scale_colour_viridis_d("p value < 0.05")+
coord_flip()+
geom_hline(yintercept = 1,color = 'black')+
theme_bw()+
scale_colour_tableau()
p
#ggsave(file=file.path(plot_folder, paste("CoxPH_Tcell-Category_Proportions_high-low_Lasso.pdf")), width=10, height=10, plot=p)
ggforest(coxdf, data = (fd_test %>% select(-contains("NA"))))
#pdf(file=file.path(plot_folder,paste("COX_selectedFactors_glmnet.pdf")))
#ggforest(coxdf, data = fd_test)
#dev.off()
```
## Cell groups categorised into "high", "mid", "low".
- high > 3rd quantile
- mid >1st quantile and < 3rd quantile
- low < 1st quantile
```{r, survival T cell category proportions classified as high mid low, message=FALSE, warning=FALSE, echo=FALSE}
surv_dat$CD4_G[surv_dat$CD4 >summary(surv_dat$CD4)[5]] <-"CD4 high"
surv_dat$CD4_G[surv_dat$CD4 <summary(surv_dat$CD4)[2]] <-"CD4 low"
surv_dat$CD4_G[surv_dat$CD4 >summary(surv_dat$CD4)[2]& surv_dat$CD4<summary(surv_dat$CD4)[5]] <-"CD4 medium"
surv_dat$CD8_G[surv_dat$CD8 >summary(surv_dat$CD8)[5]] <-"CD8 high"
surv_dat$CD8_G[surv_dat$CD8 <summary(surv_dat$CD8)[2]] <-"CD8 low"
surv_dat$CD8_G[surv_dat$CD8 >summary(surv_dat$CD8)[2]& surv_dat$CD8<summary(surv_dat$CD8)[5]] <-"CD8 medium"
surv <-c("CD8_G","CD4_G", "metacluster")
```
## Survival T cell category proportions high mid low not split
```{r, survival t cell category proportions not split high mid low, message=FALSE, warning=FALSE, echo=FALSE,out.width="50%", fig.height=4, fig.width=6}
#not split by Tumour Type (AC/SQC)
for (i in surv){
#for (k in unique(surv_dat$DX.name)){
#dat <-surv_dat[surv_dat$DX.name==k,]
dat <-surv_dat
k="AC & SCC together"
#OS
pw<-pairwise_survdiff(Surv(OS, censoringOS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(OS, censoringOS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = F,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,5000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("OS over",i,"per",k))
# facet_grid(.~surv_dat[[i]])
print(p)
#print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#km_trt_fit
#print(p)
#disease free survival
pw<-pairwise_survdiff(Surv(DFS, censoringDFS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(DFS, censoringDFS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = T,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,5000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("DFS over",i,"per",k))#+
# facet_grid(.~surv_dat[[i]])
print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#print(p)
#km_trt_fit
}
```
## Survival T cell category proportions high mid low split by tumour type
```{r, survival t cell category proportions split high mid low, message=FALSE, warning=FALSE, echo=FALSE,out.width="50%", fig.height=4, fig.width=6}
#split by Tumour Type (AC/SQC)
for (i in surv){
for (k in unique(surv_dat$DX.name)){
dat <-surv_dat[surv_dat$DX.name==k,]
dat$CD4_G[dat$CD4 >summary(dat$CD4)[5]] <-"CD4 high"
dat$CD4_G[dat$CD4 <summary(dat$CD4)[2]] <-"CD4 low"
dat$CD4_G[dat$CD4 >summary(dat$CD4)[2]& dat$CD4<summary(dat$CD4)[5]] <-"CD4 medium"
dat$CD8_G[dat$CD8 >summary(dat$CD8)[5]] <-"CD8 high"
dat$CD8_G[dat$CD8 <summary(dat$CD8)[2]] <-"CD8 low"
dat$CD8_G[dat$CD8 >summary(dat$CD8)[2]& dat$CD8<summary(dat$CD8)[5]] <-"CD8 medium"
#OS
pw<-pairwise_survdiff(Surv(OS, censoringOS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(OS, censoringOS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = F,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,2000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("OS over",i,"per",k))
# facet_grid(.~surv_dat[[i]])
print(p)
#print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#km_trt_fit
#print(p)
#disease free survival
pw<-pairwise_survdiff(Surv(DFS, censoringDFS) ~ metacluster,data = surv_dat)
#print(pw)
#print(symnum(pw$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
# symbols = c("****", "***", "**", "*", "+", "."),
# abbr.colnames = FALSE, na = " "))
km_trt_fit <- survfit(Surv(DFS, censoringDFS) ~ dat[[i]], data=dat)
km_trt_fit
p <-ggsurvplot(km_trt_fit,
data=dat,
conf.int = T,
pval = T,
#conf.int.style = "step",
surv.median.line = "hv",
xlim=c(0,2000))+ # Specify median survival,
#legend.title = paste(i),
#legend.labs=c("Grade 1","Grade 2","Grade 3"),
#legend.labs=c(paste(i,sort(unique(surv_dat[[i]][!is.na(surv_dat[[i]])])))))+
labs(title=paste("DFS over",i,"per",k))#+
# facet_grid(.~surv_dat[[i]])
print(p)
p <-p$plot+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
#facet_wrap(~strata)+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank())
#print(p)
#km_trt_fit
}
}
```
## Lasso-regressed cox-ph model
including clinical parameters, patient metacluster and T cell categories (CD4 vs CD8) classified as high mid low as well as the proportion (continuous).
```{r, coxph T cell category proportions high mid low, message=FALSE, warning=FALSE, echo=FALSE, out.width="50%", fig.width=6, fig.height=6}
surv_dat$metacluster <-as.numeric(surv_dat$metacluster)
fd_test <- fastDummies::dummy_cols(surv_dat, select_columns = c("CD4_G","CD8_G","Typ","Grade","T.new","N","M.new","Stage","Smok","Gender", "metacluster")) #
fd_test<-fd_test[!fd_test$OS == 0, ]
time <- fd_test$OS
status <- fd_test$censoringOS
rownames(fd_test) <- fd_test$Patient_ID
fd_test <-fd_test %>%select(-c("CD4_G","CD8_G","metacluster",colnames(clinical.data),contains("censor"))) #)),"AG" #,NT,"Area","TMA_ImageID"
fd_test[is.na(fd_test)] <- 0
fd_test_matrix <-data.matrix(fd_test)
#determine lambda
cv.fit <- cv.glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
fit <- glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
#plot(cv.fit)
#cv.fit$lambda.min
#loop to determine optimal lambda for lasso regression
MSEs <- NULL
for (i in 1:100){
cv <- cv.glmnet(fd_test_matrix, Surv(time, status), family = "cox", maxit = 1000)
MSEs <- cbind(MSEs, cv$cvm)
}
rownames(MSEs) <- cv$lambda
lambda.min <- as.numeric(names(which.min(rowMeans(MSEs))))
#coef(fit, s = lambda.min)
Coefficients <- coef(fit, s = lambda.min)
Active.Index <- which(Coefficients != 0)
Active.Coefficients <- Coefficients[Active.Index]
#Coefficients
active_coefficients <- Coefficients[,1] != 0
x <- fd_test_matrix[,active_coefficients]
x <- data.frame(x)
vct <-colnames(x)
coxdf<-coxph(Surv(time,status)~.,data=x)
td_res = tidy(coxdf, exponentiate = TRUE)
td_res = td_res %>% arrange(estimate) %>% mutate(term = factor(term,levels = term))
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.high = estimate + std.error)
td_res<-td_res %>%
rowwise() %>%
dplyr::mutate(conf.low = estimate - std.error)
td_res<-td_res[!td_res$std.error == 0, ]
na_td_res <- td_res[-grep("NA",td_res$term),]
p <-ggplot(na_td_res, aes(x = term, y = estimate,color =p.value < 0.05)) +
geom_point(size = 4) +
geom_errorbar(aes(ymax = conf.high, ymin = conf.low))+
scale_colour_viridis_d("p value < 0.05")+
coord_flip()+
geom_hline(yintercept = 1,color = 'black')+
theme_bw()+
scale_colour_tableau()
p
#ggsave(file=file.path(plot_folder, paste("CoxPH_Tcell-Category_Proportions_high-mid-low_Lasso.pdf")), width=10, height=10, plot=p)
ggforest(coxdf, data = (fd_test %>% select(-contains("NA"))))
#pdf(file=file.path(plot_folder,paste("COX_selectedFactors_glmnet.pdf")))
#ggforest(coxdf, data = fd_test)
#dev.off()
```
#Correlation of T cell category proportions with clinical parameters
```{r, Correlation of non T cell category proportions and clinical parameters, message=FALSE, warning=FALSE, echo=FALSE}
tcell.sce_pat$NeoAdj <- ifelse(tcell.sce_pat$Chemo==1 |tcell.sce_pat$Radio==1,"NeoAdjuvantTherapy","NoNeoAdjuvantTherapy")
tdat <-as.data.frame(table(tcell.sce_pat[,tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$Patient_ID,
tcell.sce_pat[,tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]$cell_type))
colnames(tdat)<-c("Patient_ID","Phenotype","n")
tdat <-tdat %>%
dplyr::group_by(Patient_ID) %>%
dplyr::mutate(freq = n / sum(n))
tdat[is.na(tdat)] <- 0
tdat$Phenotype <-droplevels(tdat$Phenotype)
tdat$Patient_ID <-droplevels(tdat$Patient_ID)
tdat_wide <- pivot_wider(tdat,id_cols=c("Patient_ID"),names_from = "Phenotype", values_from ="freq",names_prefix = "")
tdat_wide <-tdat_wide[!tdat_wide$Patient_ID%in% neoadj_pat$Patient_ID,]
tdat_m <-as.matrix(tdat_wide)
rownames(tdat_m)<-tdat_wide$Patient_ID
tdat_wide <-merge(tdat_wide, clinical.data, by="Patient_ID")
tdat_wide_as <-tdat_wide[tdat_wide$DX.name=="Adenocarcinoma" | tdat_wide$DX.name=="Squamous cell carcinoma",]
tdat_wide_as$LN.Met <-ifelse(tdat_wide_as$LN.Met=="LN Metastases", 1,0)
tdat_wide_as$Dist.Met <-ifelse(tdat_wide_as$Dist.Met=="Dist. Metastases", 1,0)
c.param <- c("Gender","T.new","N","M.new","Dist.Met","LN.Met","Stage")
for(i in c.param){
print(i)
print(cor.test(tdat_wide_as$CD4,tdat_wide_as[[i]], method="spearman", exact=F))
}
```
In group comparison (Wilcoxon for 2 groups or KW for >2 groups) for T cell categories over clinical variables:
- T.new (TNM)
- N (TNM)
- M.new (TNM)
- Relapse
- Grade
- Stage
- Gender
- Typ (Tumour Type)
- Dist.Met (Distant metastases yes/no)
- LN.Met (Lymph node metastases yes/no)
## Kruskal-Wallis / Wilcoxon T cell category ~ clinical data excluding neoadjuvant therapy
```{r, KW W T cell category proportions WO neo, fig.width=12, fig.height=6, message=FALSE, warning=FALSE, echo=FALSE}
df <- as.data.frame(colData(tcell.sce_pat[,tcell.sce_pat$Patient_ID!="Control"&
tcell.sce_pat$DX.name=="Adenocarcinoma"|
tcell.sce_pat$DX.name=="Squamous cell carcinoma"]))
#remove neoadj patients
df <-df[!df$Patient_ID%in% neoadj_pat$Patient_ID,]
categories <- c("Dist.Met","LN.Met", "T.new","M.new","N","Relapse","DX.name","Stage","Grade","Gender")
plot_list <- list()
for (i in (categories)) {
t <- table(df$cell_type, df$Patient_ID)
t <- prop.table(t, margin=2)
t <- as.data.frame(t)
colnames(t) <- c("Phenotype", "Patient_ID","freq")
t<-merge(t, clinical.data, by.x="Patient_ID", by.y="Patient_ID")
tdat <-t
tdat$i <-as.factor(tdat[[i]])
tdat <-tdat %>% drop_na(i)
tdat$Phenotype <-droplevels(tdat$Phenotype)
for (j in unique(t$Phenotype)){
tdat<-t
tdat[[i]] <-as.factor(tdat[[i]])
tdat <-tdat %>% drop_na(i)
tdat$i <-as.factor(tdat[[i]])
tdat$Phenotype <-droplevels(tdat$Phenotype)
tdat <-subset(tdat, Phenotype==j)
if (length(unique(tdat[[i]])) > 2)
{
#calculate p values, add position for plot
stat.test <- tdat%>%
group_by(Phenotype) %>%
dunn_test(freq~i,p.adjust.method = "bonferroni")
stat.test<-stat.test %>% select(-.y., -statistic)
stat.test <- stat.test %>% add_xy_position(x = i)
#create plot list
plot_list[[j]]<-
ggplot(tdat, aes(x= .data[[i]], y = freq, colour=.data[[i]]))+
geom_boxplot()+
geom_point()+
facet_wrap(~Phenotype, scales="free", ncol=4, strip.position="top")+
scale_color_viridis_d()+
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
labs( y="Proportion")+
theme(axis.text.x = element_text(angle=45, hjust=1), axis.ticks.x = element_blank(),legend.position = "none")+
stat_pvalue_manual(stat.test, label = "p.adj.signif", tip.length = 0, hide.ns = T) }
else {
if(is.element(i,colnames(clinical.data)) == TRUE){
tdat<-t
tdat[[i]] <-as.factor(tdat[[i]])
tdat <-tdat %>% drop_na(i)