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05_cluster_stroma-cells.Rmd
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05_cluster_stroma-cells.Rmd
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---
title: "R Notebook - Analyse Stromal 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 all stroma, message=F, echo=F, warning=F}
#set working directory
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","stroma"))
#RAW
#all.stroma <- readRDS(file=file.path(data_folder, "all_stroma_RAW.rds"))
#workingfile
all.stroma <- readRDS(file=file.path(data_folder, "all_stroma_workingfile.rds"))
#saveRDS(all.stroma, file=file.path(data_folder, "all_stroma_RAW.rds"))
#saveRDS(all.stroma, file=file.path(data_folder, "all_stroma_workingfile.rds"))
```
```{r, Define stroma markers, echo=F, warning=F, message=FALSE}
all.marker <-rownames(all.stroma)
bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3")
good.marker <- all.marker[!all.marker %in% bad.marker]
stroma.marker <-c("FSP1 / S100A4","SMA","FAP","Cadherin-11","Carbonic Anhydrase IX","Collagen I + Fibronectin",
"VCAM1","Indoleamine 2- 3-dioxygenase (IDO)","Podoplanin","MMP11","CD73","MMP9","p75 (CD271)","CD10","Vimentin","CD248 / Endosialin","PDGFR-b","CD34","CXCL12","CCL21","Ki-67","Caveolin-1","CD146",
"vWF + CD31","LYVE-1" , "PNAd" )
print(stroma.marker)
```
```{r, subset all.stroma, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(all.stroma)), all.stroma$Tma_ac)
length(unique(all.stroma$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.stroma.sub <- all.stroma[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(all.stroma.sub))[2]/dim(assay(all.stroma))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
all.stroma.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(all.stroma.sub, file=file.path(data_folder, paste("all_stroma_sub.rds")))
```
```{r, load subset, message=FALSE, warning=FALSE, echo=FALSE}
all.stroma.sub <- readRDS(file=file.path(data_folder, paste("all_stroma_sub.rds")))
```
```{r, calculate umap all stroma sub, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
all.stroma.sub <- runUMAP(all.stroma.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(all.stroma.sub, file=file.path(data_folder, paste("all_stroma_sub.rds")))
}
saveRDS(all.stroma.sub, file=file.path(data_folder, paste("all_stroma_sub.rds")))
for(i in p){
all.stroma.sub <- runTSNE(all.stroma.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(all.stroma.sub, file=file.path(data_folder, paste("all_stroma_sub.rds")))
}
```
**UMAP with good markers**
```{r,plot umap all.stroma.sub. good marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(all.stroma.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.stroma.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")
dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")
p <-dat.all.long %>%
ggplot(aes(x=V1, y=V2, color=counts))+
facet_wrap(~target, scales = "free", ncol = 6)+
geom_point(alpha=0.5, size=0.2)+
scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
ggtitle('')+
theme(strip.background = element_blank(),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
strip.text = element_text(size=8))
##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```
**tsne with Tumour markers**
```{r,plot tsne all stroma sub good marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE, eval=F}
dat <-as.data.frame(reducedDims(all.stroma.sub)$`tSNE_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.stroma.sub,"c_counts_asinh_scaled"))))
dat.counts$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 stroma 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.stroma$RPmembership <- factor(Rphenoannoy(data = t(assay(all.stroma[rownames(all.stroma) %in% good.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
cluster <- paste0("rp_stroma_all_k",i)
colnames(colData(all.stroma))[which(names(colData(all.stroma)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(all.stroma, ids=all.stroma[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = good.marker,
#features = all.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.stroma, file=file.path(data_folder, "all_stroma_workingfile.rds"))
}
#all.stroma <-readRDS( file=file.path(data_folder, "all_stroma_workingfile.rds"))
```
```{r, categorise all stroma clusters, message=F, echo=F, warning=F}
is.tumour <- c(68)
is.immune <- c(23,53)
not.stroma <- c(68,23,53)
all.stroma.tumour <- all.stroma[, all.stroma$rp_stroma_all_k20 %in% is.tumour]
all.stroma.immune <- all.stroma[, all.stroma$rp_stroma_all_k20 %in% is.immune]
all.stroma.final <- all.stroma[, !all.stroma$rp_stroma_all_k20 %in% not.stroma]
#saveRDS(all.stroma.tumour, file=file.path(data_folder, "all_stroma_workingfile_TUMOUR.rds"))
#saveRDS(all.stroma.immune, file=file.path(data_folder, "all_stroma_workingfile_IMMUNE.rds"))
#saveRDS(all.stroma.final, file=file.path(data_folder, "FINAL_all_stroma_RAW.rds"))
#stroma.final <-all.stroma.final
#rm(all.stroma.final, all.stroma.immune, all.stroma.tumour)
```
################################################################################################################################
#carrying on using stroma.final from here
```{r, read in final stroma, message=F, echo=F, warning=F}
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","stroma"))
#RAW
#stroma.final <-readRDS(file=file.path(data_folder, "FINAL_all_stroma_RAW.rds"))
#Workingfile
stroma.final <-readRDS(file=file.path(data_folder, "FINAL_all_stroma_workingfile.rds"))
#saveRDS(stroma.final,file=file.path(data_folder,"FINAL_all_stroma_workingfile.rds"))
stroma.marker <-c("FSP1 / S100A4","SMA","FAP","Cadherin-11","Carbonic Anhydrase IX","Collagen I + Fibronectin",
"VCAM1","Indoleamine 2- 3-dioxygenase (IDO)","Podoplanin","MMP11","CD73","MMP9","p75 (CD271)",
"CD10","Vimentin","CD248 / Endosialin","PDGFR-b","CD34","CXCL12","CCL21","Ki-67","Caveolin-1",
"CD146", "vWF + CD31","LYVE-1" , "PNAd" )
```
```{r, subset stroma final, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(stroma.final)), stroma.final$Tma_ac)
length(unique(stroma.final$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u)
sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
stroma.final.sub <- stroma.final[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(stroma.final.sub))[2]/dim(assay(stroma.final))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
stroma.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(stroma.final.sub, file=file.path(data_folder, paste("stroma_final_sub.rds")))
```
```{r, load final stroma subset, message=FALSE, warning=FALSE, echo=FALSE}
stroma.final.sub <- readRDS(file=file.path(data_folder, paste("stroma_final_sub.rds")))
```
```{r, calculate umap final stroma, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
stroma.final.sub <- runUMAP(stroma.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(stroma.final.sub, file=file.path(data_folder, paste("stroma_final_sub.rds")))
}
saveRDS(stroma.final.sub, file=file.path(data_folder, paste("stroma_final_sub.rds")))
for(i in p){
stroma.final.sub <- runTSNE(stroma.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(stroma.final.sub, file=file.path(data_folder, paste("stroma_final_sub.rds")))
}
```
**UMAP with good markers**
```{r,plot umap final stroma stroma marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(stroma.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(stroma.final.sub,"c_counts_asinh_scaled"))))
dat.counts$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% stroma.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 stroma final,using Rphenoannoy, fig.width=25, fig.height=12, message=F, echo=F, warning=F, eval=F}
stroma.final$rp_stroma_all_k20 <-NULL
stroma.final$`rp_stroma_all-stroma-marker_k20` <-NULL
stroma.final$`rp_NONtumour-final_k35` <-NULL
stroma.final$`rp_NONtumour-final_k40` <-NULL
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(20,25,30,35,40)
#i<-10
for (i in k) {
# stroma.final$RPmembership <- factor(Rphenoannoy(data = t(assay(stroma.final[rownames(stroma.final) %in% stroma.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
cluster <- paste0("rp_stroma_final_k",i)
# colnames(colData(stroma.final))[which(names(colData(stroma.final)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(stroma.final, ids=stroma.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = stroma.marker,
#features = all.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(stroma.final,file=file.path(data_folder,"FINAL_all_stroma_workingfile.rds"))
}
#stroma.final <-readRDS( file=file.path(data_folder, "final_stroma_workingfile.rds"))
```
```{r,HM stroma final, fig.width=25, fig.height=12, message=F, echo=F, warning=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))
is.vessel <- c(40,34,14,22,47,50,31,23,46,49,27,16,32,43,48,11,62,23,61,10)
stroma.final$vessel <- ifelse(stroma.final$rp_stroma_final_k20 %in% is.vessel, "vessel","CAF")
agg_sce <-aggregateAcrossCells(stroma.final, ids=stroma.final$rp_stroma_final_k20, average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = stroma.marker,
#features = all.marker,
#features = marker.clustering,
exprs_values = "c_counts_asinh_scaled",
symmetric = FALSE,
zlim=c(0,1),
color=rdylbu,
sortrowss=FALSE,
show_colnames=TRUE,
color_columns_by = c("vessel"))
stroma.vessel <- stroma.final[, stroma.final$vessel =="vessel"]
stroma.CAF <- stroma.final[, stroma.final$vessel =="CAF"]
#saveRDS(stroma.vessel, file=file.path(data_folder, "vessel","stroma_final_VESSEL_workingfile.rds"))
#saveRDS(stroma.vessel, file=file.path(data_folder, "vessel","stroma_final_VESSEL_RAW.rds"))
#saveRDS(stroma.vessel, file=file.path(data_folder,"stroma_final_VESSEL.rds"))
#saveRDS(stroma.CAF, file=file.path(data_folder,"stroma_final_CAF.rds"))
```