04_02_cluster_Tcells.Rmd

---
title: "R Notebook - Cluster T cells"
output:
  html_document:
    df_print: paged
---

```{r, import libraries, message=F, echo=F, warning=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)
set.seed(101100)
```


```{r, Set wd and load data}
#set working directory 
wd <-dirname(getwd())

data_folder <-(file.path(wd,"sce_objects","immune","Tcell"))

#RAW
#all.tcell <- readRDS(file=file.path(data_folder, "tcells_RAW.rds"))

#workingfile
all.tcell <- readRDS(file=file.path(data_folder, "all_tcell_workingfile.rds"))
#saveRDS(all.tcell, file=file.path(data_folder, "all_tcell_workingfile.rds"))
```


```{r, Define tcell markers, echo=F, warning=F, message=FALSE}
all.marker <-rownames(all.tcell)

bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3") 
good.marker <- all.marker[!all.marker %in% bad.marker]
btcell.marker <-c("CD20","Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4"  ,"Ki-67","CD279 (PD-1)") #to kick out final B cells from merge cluster

tcell.marker <-c("Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4"  ,"Ki-67","CD279 (PD-1)")

print(tcell.marker)
```

```{r, subset tcell all, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(all.tcell)), all.tcell$Tma_ac)
length(unique(all.tcell$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u) 
  sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
all.tcell.sub <- all.tcell[,cs]

#calculate percentage of cells subsetting results in
p<-dim(assay(all.tcell.sub))[2]/dim(assay(all.tcell))[2]*100

#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
  all.tcell.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))

saveRDS(all.tcell.sub, file=file.path(data_folder, paste("all_tcell_sub.rds")))
```

```{r, load subset all tcells, message=FALSE, warning=FALSE, echo=FALSE}
all.tcell.sub <- readRDS(file=file.path(data_folder, paste("all_tcell_sub.rds")))
```

```{r, calculate umap all tcells, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
all.tcell.sub <- runUMAP(all.tcell.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("UMAP_p", i),
                 #use_dimred="PCA_20",
                 n_neighbors = i)
saveRDS(all.tcell.sub, file=file.path(data_folder, paste("all_tcell_sub.rds")))

}
saveRDS(all.tcell.sub, file=file.path(data_folder, paste("all_tcell_sub.rds")))


for(i in p){
all.tcell.sub <- runTSNE(all.tcell.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("tSNE_p", i),
                 #use_dimred="PCA_20",
                 perplexity = i)
saveRDS(all.tcell.sub, file=file.path(data_folder, paste("all_tcell_sub.rds")))
}
```


**UMAP with good markers**
```{r,plot umap all t cells immune marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(all.tcell.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.tcell.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")

dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% btcell.marker], names_to = "target", values_to = "counts")


p <-dat.all.long %>%
  ggplot(aes(x=V1, y=V2, color=counts))+
  facet_wrap(~target, scales = "free", ncol = 6)+
  geom_point(alpha=0.5, size=0.2)+
  scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
  ggtitle('')+
  theme(strip.background = element_blank(),
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        panel.background=element_blank(),
        panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        plot.background=element_blank(),
         strip.text = element_text(size=8)) 

##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```

**tsne with Tumour markers**
```{r,plot tsne t cell marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE,eval=F}
dat <-as.data.frame(reducedDims(all.tcell.sub)$`tSNE_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.tcell.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")

dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")


p <-dat.all.long %>%
  ggplot(aes(x=V1, y=V2, color=counts))+
  facet_wrap(~target, scales = "free", ncol = 6)+
  geom_point(alpha=0.5, size=0.2)+
  scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
  ggtitle('')+
  theme(strip.background = element_blank(),
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        panel.background=element_blank(),
        panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        plot.background=element_blank(),
         strip.text = element_text(size=8)) 

##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```


```{r Clustering all tcell using Rphenoannoy, fig.width=25, fig.height=12 , message=F, echo=F, warning=F, eval=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))

#marker.clustering <- good.marker[!good.marker %in% "Pan Cytokeratin + Keratin Epithelial"]
#cellnb <- 250000
#subsample <-  colnames(sce)[sample(length(colnames(sce)), cellnb)]
#sce_sub <- sce[, colnames(sce) %in% subsample]

#set ks for clustering
#i <- 100
k <- c(20)
#i<-10
for (i in k) {
    all.tcell$RPmembership <- factor(Rphenoannoy(data = t(assay(all.tcell[rownames(all.tcell) %in% immune.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
    cluster <- paste0("rp_tcell_immune_k",i)
    cluster <- paste0("rp_tcell_tb_k",i)
  colnames(colData(all.tcell))[which(names(colData(all.tcell)) == "RPmembership")] <- paste0(cluster)
  agg_sce <-aggregateAcrossCells(all.tcell, ids=all.tcell[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )

 
    scater::plotHeatmap(agg_sce,
              features = btcell.marker,
              #features = immune.marker,
              #features = marker.clustering,
              exprs_values = "c_counts_asinh_scaled",
              symmetric = FALSE,
              zlim=c(0,1),
              color=rdylbu,
              sortrowss=FALSE,
              show_colnames=TRUE,
              color_columns_by = c(cluster),
              main=paste0("Heatmap tumour cells, ",cluster))
    
  #save sce clustering
  saveRDS(all.tcell, file=file.path(data_folder, "all_tcell_workingfile.rds"))
}
#all.tcell <-readRDS( file=file.path(data_folder, "all_tcell_workingfile.rds"))

```
```{r, add clustering results to subset all t cells, message=F, echo=F, warning=F, eval=F}
rp_df <- data.frame("CellID"=all.tcell$CellID, "rp_tcell_tb_k20"=all.tcell$rp_tcell_tb_k20)

cur_DF <- as_tibble(colData(all.tcell.sub)) %>% left_join(rp_df, by = "CellID") %>% DataFrame()
all.equal(paste(cur_DF$ImageNumber, cur_DF$CellNumber), paste(all.tcell.sub$ImageNumber, all.tcell.sub$CellNumber))

colData(all.tcell.sub) <- cur_DF
rownames(colData(all.tcell.sub)) <-all.tcell.sub$CellID

```

```{r, plot all t cell sub cluster on umap, message=F, echo=F, warning=F, eval=F}
cluster <- "rp_tcell_tb_k20"
p <-plotReducedDim(all.tcell.sub[, all.tcell.sub$rp_tcell_tb_k20==17], "UMAP_p50", colour_by=paste(cluster), text_by = paste(cluster),text_colour ="red", point_size=0.5,)+labs(x="UMAP-1", y="UMAP-2", title="UMAP") 
plot(p)
```
```{r, assign labels, B cells and T cells, message=F, echo=F, warning=F}
is.bcell <-17
all.tcell.bcell <- all.tcell[, all.tcell$rp_tcell_tb_k20==17]
#saveRDS(all.tcell.bcell, file=file.path(data_folder, "tcells_bcells.RDS"))

tcell.final <- all.tcell[, all.tcell$rp_tcell_tb_k20 != 17]
#saveRDS(tcell.final, file=file.path(data_folder, "tcell_final_workingfile.RDS"))

tcell.final$rp_tcell_immune_k20 <-NULL
colnames(colData(tcell.final))
```


#################
#T cells final

```{r, Set wd and load data t cell final, message=F, echo=F, warning=F}
#set working directory 
wd <-dirname(getwd())

data_folder <-(file.path(wd,"sce_objects","immune","Tcell"))

#RAW
tcell.final <- readRDS(file=file.path(data_folder, "tcell_final.RDS"))

#workingfile
tcell.final <- readRDS(file=file.path(data_folder, "tcell_final_workingfile.rds"))
#saveRDS(tcell.final, file=file.path(data_folder, "tcell_final_workingfile.rds"))

colnames(colData(tcell.final))
```


```{r, Define tcell markers final, echo=F, warning=F, message=FALSE}
all.marker <-rownames(tcell.final)

bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3") 
good.marker <- all.marker[!all.marker %in% bad.marker]

tcell.marker <-c("Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4"  ,"Ki-67","CD279 (PD-1)")

print(tcell.marker)
```

```{r, subset tcell final, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(tcell.final)), tcell.final$Tma_ac)
length(unique(tcell.final$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u) 
  sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
tcell.final.sub <- tcell.final[,cs]

#calculate percentage of cells subsetting results in
p<-dim(assay(tcell.final.sub))[2]/dim(assay(tcell.final))[2]*100

#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
  tcell.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))

saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final_sub.rds")))
```

```{r, load subset t cell final, message=FALSE, warning=FALSE, echo=FALSE}
tcell.final.sub <- readRDS(file=file.path(data_folder, paste("tcell_final_sub.rds")))
```

```{r, calculate umap t cell final, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
tcell.final.sub <- runUMAP(tcell.final.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("UMAP_p", i),
                 #use_dimred="PCA_20",
                 n_neighbors = i)
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final_sub.rds")))

}
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final_sub.rds")))


for(i in p){
tcell.final.sub <- runTSNE(tcell.final.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("tSNE_p", i),
                 #use_dimred="PCA_20",
                 perplexity = i)
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final_sub.rds")))
}
```


**UMAP with good markers**
```{r,plot umap t cell final t cell marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(tcell.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(tcell.final.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")

dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")


p <-dat.all.long %>%
  ggplot(aes(x=V1, y=V2, color=counts))+
  facet_wrap(~target, scales = "free", ncol = 6)+
  geom_point(alpha=0.5, size=0.2)+
  scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
  ggtitle('')+
  theme(strip.background = element_blank(),
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        panel.background=element_blank(),
        panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        plot.background=element_blank(),
         strip.text = element_text(size=8)) 

##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```


```{r, Clustering t cell final using Rphenoannoy, fig.width=25, fig.height=12, message=F, echo=F, warning=F, eval=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))

#marker.clustering <- good.marker[!good.marker %in% "Pan Cytokeratin + Keratin Epithelial"]
#cellnb <- 250000
#subsample <-  colnames(sce)[sample(length(colnames(sce)), cellnb)]
#sce_sub <- sce[, colnames(sce) %in% subsample]

#set ks for clustering
#i <- 100
k <- c(35)
#i<-30
for (i in k) {
    tcell.final$RPmembership <- factor(Rphenoannoy(data = t(assay(tcell.final[rownames(tcell.final) %in% tcell.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
    cluster <- paste0("rp_tcell_k",i)
  colnames(colData(tcell.final))[which(names(colData(tcell.final)) == "RPmembership")] <- paste0(cluster)
  agg_sce <-aggregateAcrossCells(tcell.final, ids=tcell.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )

 
    scater::plotHeatmap(agg_sce,
              features = tcell.marker,
              #features = immune.marker,
              #features = marker.clustering,
              exprs_values = "c_counts_asinh_scaled",
              symmetric = FALSE,
              zlim=c(0,1),
              color=rdylbu,
              sortrowss=FALSE,
              show_colnames=TRUE,
              color_columns_by = c(cluster),
              main=paste0("Heatmap tumour cells, ",cluster))
    
  #save sce clustering
  saveRDS(tcell.final, file=file.path(data_folder, "tcell_final_workingfile.rds"))
}
#tcell.final <-readRDS( file=file.path(data_folder, "all_tcell_workingfile.rds"))

```
```{r, assign cluster levels T cell final,fig.width=12, fig.height=10, message=F, echo=F, warning=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))

is.immune <- c(1,9,3)
is.CD8 <- c(10,20,6,21,12,13,7,5)
is.CD8.TCF <- c(22,19)
is.CD8.IDO <- c(17)
is.CD8.ki67 <- c(2)
is.CD4.PD1 <-c(23)
is.CD4.Treg <-c(18)
is.CD4 <-c(4,11,14)
is.CD4.ki67 <-c(8)
is.CD4.TCF <-c(16)
is.CD4.IDO <-c(15)

tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.immune] <-"Immune"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD8] <-"CD8"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD8.TCF] <-"TCF1/7_CD8"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD8.IDO] <-"IDO_CD8"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD8.ki67] <-"ki67_CD8"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4.PD1] <-"PD1_CD4"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4.Treg] <-"CD4_Treg"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4] <-"CD4"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4.ki67] <-"ki67_CD4"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4.TCF] <-"TCF1/7_CD4"
tcell.final$TcellType[tcell.final$rp_tcell_k30%in% is.CD4.IDO] <-"IDO_CD4"

agg_sce <-aggregateAcrossCells(tcell.final, ids=tcell.final$rp_tcell_k30, average=TRUE, use_exprs_values="c_counts_asinh_scaled" )

 
    scater::plotHeatmap(agg_sce,
              features = tcell.marker,
              #features = immune.marker,
              #features = marker.clustering,
              exprs_values = "c_counts_asinh_scaled",
              symmetric = FALSE,
              zlim=c(0,1),
              color=rdylbu,
              sortrowss=FALSE,
              show_colnames=TRUE,
              color_columns_by = c("TcellType"))
    
tcell.final.immune <- tcell.final[, tcell.final$TcellType=="Immune"]
#saveRDS(tcell.final.immune, file=file.path(data_folder, "tcell-final-immune.rds"))
tcell.final <- tcell.final[, tcell.final$TcellType!="Immune"]

#saveRDS(tcell.final, file=file.path(data_folder, "FINAL_Tcells-minusImmune_RAW.rds"))
#saveRDS(tcell.final, file=file.path(data_folder, "FINAL_Tcells-minusImmune_workingfile.rds"))

tcell.final$TcellCategory <- ifelse(tcell.final$rp_tcell_k30 %in%is.CD8 |
                                      tcell.final$rp_tcell_k30 %in%is.CD8.TCF|
                                      tcell.final$rp_tcell_k30 %in%is.CD8.IDO|
                                      tcell.final$rp_tcell_k30 %in%is.CD8.ki67,
                                    "CD8","CD4")

table(tcell.final$TcellCategory)
table(tcell.final$TcellType)
```

```{r, subset tcell final minus immune, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(tcell.final)), tcell.final$Tma_ac)
length(unique(tcell.final$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u) 
  sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
tcell.final.sub <- tcell.final[,cs]

#calculate percentage of cells subsetting results in
p<-dim(assay(tcell.final.sub))[2]/dim(assay(tcell.final))[2]*100

#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
  tcell.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))

saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final-minusImmune_sub.rds")))
```

```{r, load subset t cells minus immune, message=FALSE, warning=FALSE, echo=FALSE}
tcell.final.sub <- readRDS(file=file.path(data_folder, paste("tcell_final-minusImmune_sub.rds")))
```

```{r, calculate umap t cells minus immune, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
tcell.final.sub <- runUMAP(tcell.final.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("UMAP_p", i),
                 #use_dimred="PCA_20",
                 n_neighbors = i)
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final-minusImmune_sub.rds")))

}
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final-minusImmune_sub.rds")))


for(i in p){
tcell.final.sub <- runTSNE(tcell.final.sub,
                 exprs_values = "c_counts_asinh",
                 name = paste0("tSNE_p", i),
                 #use_dimred="PCA_20",
                 perplexity = i)
saveRDS(tcell.final.sub, file=file.path(data_folder, paste("tcell_final-minusImmune_sub.rds")))
}
```


**UMAP with good markers**
```{r,plot umap t cell minus immune t cell marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(tcell.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(tcell.final.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")

dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% tcell.marker], names_to = "target", values_to = "counts")


p <-dat.all.long %>%
  ggplot(aes(x=V1, y=V2, color=counts))+
  facet_wrap(~target, scales = "free", ncol = 6)+
  geom_point(alpha=0.5, size=0.2)+
  scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
  ggtitle('')+
  theme(strip.background = element_blank(),
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        panel.background=element_blank(),
        panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        plot.background=element_blank(),
         strip.text = element_text(size=8)) 

##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```

```{r, plot umap with t cell category and types minus immune, message=F, warning=F, echo=F}
#Tell Category
cluster<- "TcellCategory"
p <-plotReducedDim(tcell.final.sub, "UMAP_p50", colour_by=paste(cluster), text_by = paste(cluster),text_colour ="red", point_size=0.5,)+labs(x="UMAP-1", y="UMAP-2", title="UMAP") 
plot(p)

#Tell Type
cluster<- "TcellType"
p <-plotReducedDim(tcell.final.sub, "UMAP_p50", colour_by=paste(cluster), text_by = paste(cluster),text_colour ="red", point_size=0.5,)+labs(x="UMAP-1", y="UMAP-2", title="UMAP") 
plot(p)
```