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04_cluster_immune-cells.Rmd
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04_cluster_immune-cells.Rmd
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---
title: "R Notebook - Analyse Immune cells"
output:
html_document:
df_print: paged
---
```{r, import libraries,echo=F, warning=FALSE, message=FALSE}
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,echo=F, warning=FALSE, message=FALSE}
#set working directory
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","immune"))
#RAW
#all.immune <- readRDS(file=file.path(data_folder, "all_immune_RAW.rds"))
#workingfile
all.immune <- readRDS(file=file.path(data_folder, "all_immune_workingfile.rds"))
#saveRDS(all.immune, file=file.path(results_folder, "all_immune_RAW.rds"))
#saveRDS(all.immune, file=file.path(data_folder, "all_immune_workingfile.rds"))
```
```{r, Define immune markers, echo=F, warning=F, message=FALSE}
all.marker <-rownames(all.immune)
bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3")
good.marker <- all.marker[!all.marker %in% bad.marker]
immune.marker <-c("Myeloperoxidase (MPO)" ,"HLA-DR","CD20","CD68","Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4" ,"CD15" ,"Ki-67","CD279 (PD-1)")
print(immune.marker)
```
```{r, subset all.immune,echo=F, warning=FALSE, message=FALSE, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(all.immune)), all.immune$Tma_ac)
length(unique(all.immune$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.immune.sub <- all.immune[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(all.immune.sub))[2]/dim(assay(all.immune))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
all.immune.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(all.immune.sub, file=file.path(data_folder, paste("all_immune_sub.rds")))
```
```{r, load subset, message=FALSE, warning=FALSE, echo=FALSE}
all.immune.sub <- readRDS(file=file.path(data_folder, paste("all_immune_sub.rds")))
```
```{r, calculate umap, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
all.immune.sub <- runUMAP(all.immune.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(all.immune.sub, file=file.path(data_folder, paste("all_immune_sub.rds")))
}
saveRDS(all.immune.sub, file=file.path(data_folder, paste("all_immune_sub.rds")))
for(i in p){
all.immune.sub <- runTSNE(all.immune.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(all.immune.sub, file=file.path(data_folder, paste("all_immune_sub.rds")))
}
```
**UMAP with good markers**
```{r,plot umap tumour marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(all.immune.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.immune.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")
dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")
p <-dat.all.long %>%
ggplot(aes(x=V1, y=V2, color=counts))+
facet_wrap(~target, scales = "free", ncol = 6)+
geom_point(alpha=0.5, size=0.2)+
scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
ggtitle('')+
theme(strip.background = element_blank(),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
strip.text = element_text(size=8))
##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```
**tsne with immune markers**
```{r,plot tsne tumour marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE, eval=F}
dat <-as.data.frame(reducedDims(all.immune.sub)$`tSNE_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(all.immune.sub,"c_counts_asinh_scaled"))))
dat.counts$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 using Rphenoannoy,echo=F, warning=FALSE, message=FALSE,eval=F, fig.width=25, fig.height=12}
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.immune$RPmembership <- factor(Rphenoannoy(data = t(assay(all.immune[rownames(all.immune) %in% good.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
cluster <- paste0("rp_immune_all_k",i)
colnames(colData(all.immune))[which(names(colData(all.immune)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(all.immune, ids=all.immune[[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.immune, file=file.path(data_folder, "all_immune_workingfile.rds"))
}
#all.immune <-readRDS( file=file.path(data_folder, "all_immune_workingfile.rds"))
```
```{r, HM all immune tumour, fig.width=20, fig.height=10, message=F, warning=F, echo=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))
table(all.immune$rp_immune_all_k20)
is.tumour <- c(60,22,73,71)
all.immune$tumour_immune <-ifelse(all.immune$rp_immune_all_k20 %in% is.tumour, "tumour","immune")
table(all.immune$tumour_immune)
cluster="rp_immune_all_k20"
agg_sce <-aggregateAcrossCells(all.immune, ids=all.immune[[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,"tumour_immune"),
main=paste0("Heatmap tumour cells, ",cluster))
immune.final <- all.immune[, all.immune$tumour_immune=="immune"]
immune.tumour <- all.immune[, all.immune$tumour_immune=="tumour"]
#saveRDS(immune.final, file=file.path(data_folder, "final_immune_RAW.rds"))
#saveRDS(immune.tumour, file=file.path(data_folder, "all_immune_workingfile_TUMOUR.rds"))
```
################################################################################################################################
#carrying on using immune.final from here
```{r, immune final read in, message=F, warning=F, echo=F}
#immune.final <-readRDS(file=file.path(data_folder, "final_immune_RAW.rds"))
#saveRDS(immune.final,file=file.path(data_folder, "final_immune_workingfile.rds"))
immune.marker <-c("Myeloperoxidase (MPO)" ,"HLA-DR","CD20","CD68","Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4" ,"CD15" ,"Ki-67","CD279 (PD-1)")
```
```{r, subset immune.final, message=F, warning=F, echo=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(immune.final)), immune.final$Tma_ac)
length(unique(immune.final$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]
immune.final.sub <- immune.final[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(immune.final.sub))[2]/dim(assay(immune.final))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
immune.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub.rds")))
```
```{r, load subset immune.final, message=FALSE, warning=FALSE, echo=FALSE}
immune.final.sub <- readRDS(file=file.path(data_folder, paste("immune_final_sub.rds")))
```
```{r, calculate umap immune.final, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
immune.final.sub <- runUMAP(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub.rds")))
}
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub.rds")))
for(i in p){
immune.final.sub <- runTSNE(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub.rds")))
}
```
**UMAP with good markers**
```{r,plot umap immune.final sub immune marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(immune.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(immune.final.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")
dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")
p <-dat.all.long %>%
ggplot(aes(x=V1, y=V2, color=counts))+
facet_wrap(~target, scales = "free", ncol = 6)+
geom_point(alpha=0.5, size=0.2)+
scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
ggtitle('')+
theme(strip.background = element_blank(),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
strip.text = element_text(size=8))
##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```
```{r Clustering immune.final using Rphenoannoy, fig.width=25, fig.height=12, message=F, warning=F, echo=F, eval=F}
immune.final$rp_immune_all_k20 <-NULL
immune.final$`rp_NONtumour-final_k30` <-NULL
immune.final$`rp_NONtumour-final_k35` <-NULL
immune.final$`rp_NONtumour-final_k40` <-NULL
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))
#marker.clustering <- good.marker[!good.marker %in% "Pan Cytokeratin + Keratin Epithelial"]
#cellnb <- 250000
#subsample <- colnames(sce)[sample(length(colnames(sce)), cellnb)]
#sce_sub <- sce[, colnames(sce) %in% subsample]
#set ks for clustering
#i <- 100
k <- c(20)
#i<-10
for (i in k) {
immune.final$RPmembership <- factor(Rphenoannoy(data = t(assay(immune.final[rownames(immune.final) %in% immune.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
cluster <- paste0("rp_immune_final_k",i)
colnames(colData(immune.final))[which(names(colData(immune.final)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = good.marker,
#features = all.marker,
#features = marker.clustering,
exprs_values = "c_counts_asinh_scaled",
symmetric = FALSE,
zlim=c(0,1),
color=rdylbu,
sortrowss=FALSE,
show_colnames=TRUE,
color_columns_by = c(cluster),
main=paste0("Heatmap tumour cells, ",cluster))
#save sce clustering
saveRDS(immune.final, file=file.path(data_folder, "final_immune_workingfile.rds"))
}
#immune.final <-readRDS( file=file.path(data_folder, "final_immune_workingfile.rds"))
immune.final <-readRDS( file=file.path(wd,"sce_objects","immune", "final_immune_workingfile.rds"))
```
##########################
immune + stroma.immune
#add immune cells from stromal clustering
```{r, merge immune cells from stromal clustering, message=F, warning=F, echo=F, eval=F}
all.stroma.immune <-readRDS(file=file.path(wd,"sce_objects","stroma", "all_stroma_workingfile_IMMUNE.rds"))
colnames(colData(all.stroma.immune))[!colnames(colData(all.stroma.immune)) %in%colnames(colData(immune.final))]
all.stroma.immune$`rp_NONtumour-final_k30` <-NULL
all.stroma.immune$`rp_NONtumour-final_k35` <-NULL
all.stroma.immune$`rp_NONtumour-final_k40` <-NULL
all.stroma.immune$`rp_stroma_all-stroma-marker_k20` <-NULL
all.stroma.immune$rp_stroma_all_k20 <-NULL
colnames(colData(immune.final))[!colnames(colData(immune.final)) %in%colnames(colData(all.stroma.immune))]
immune.final$rp_immune_all_k20 <-NULL
immune.final$tumour_immune <-NULL
immune.final$rp_immune_final_k20 <-NULL
immune.final <- cbind(immune.final, all.stroma.immune)
saveRDS(immune.final, file=file.path(data_folder, "final_immune_RAW_afterStromaMerge.rds"))
```
```{r, read immune final after immune stroma merge, message=F, warning=F, echo=F}
immune.final <-readRDS(file=file.path(data_folder, "final_immune_RAW_afterStromaMerge.rds"))
immune.final <-readRDS(file=file.path(data_folder, "final_immune_afterStromaMerge_workingfile.rds"))
#saveRDS(immune.final,file=file.path(data_folder, "final_immune_afterStromaMerge_workingfile.rds"))
immune.marker <-c("Myeloperoxidase (MPO)" ,"HLA-DR","CD20","CD68","Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4" ,"CD15" ,"Ki-67","CD279 (PD-1)")
```
```{r, subset immune final after stroma merge, message=F, warning=F, echo=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(immune.final)), immune.final$Tma_ac)
length(unique(immune.final$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u)
sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
immune.final.sub <- immune.final[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(immune.final.sub))[2]/dim(assay(immune.final))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
immune.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub_aftermerge.rds")))
```
```{r, load subset immune final after stroma merge, message=FALSE, warning=FALSE, echo=FALSE}
immune.final.sub <- readRDS(file=file.path(data_folder, paste("immune_final_sub.rds")))
```
```{r, calculate umap immune final after stroma merge, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
immune.final.sub <- runUMAP(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub_aftermerge.rds")))
}
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub_aftermerge.rds")))
for(i in p){
immune.final.sub <- runTSNE(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("immune_final_sub_aftermerge.rds")))
}
```
**UMAP with good markers**
```{r,plot umap immune after stroma merge immune marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(immune.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(immune.final.sub,"c_counts_asinh_scaled"))))
dat.counts$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% immune.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 immune final after stroma merge using Rphenoannoy, fig.width=25, fig.height=12, message=F, warning=F, echo=F, eval=F}
immune.final$cell_category <-NULL
immune.final$tumour_nontumour <-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(15)
#i<-20
for (i in k) {
immune.final$RPmembership <- factor(Rphenoannoy(data = t(assay(immune.final[rownames(immune.final) %in% immune.marker,],"c_counts_asinh")), k = i)[[2]]$membership)
cluster <- paste0("rp_immune_final_k",i)
colnames(colData(immune.final))[which(names(colData(immune.final)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = immune.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 immune cells, ",cluster))
#save sce clustering
saveRDS(immune.final,file=file.path(data_folder, "final_immune_afterStromaMerge_workingfile.rds"))
}
#immune.final <-readRDS( file=file.path(wd,"sce_objects","immune", "final_immune_afterStromaMerge_workingfile.rds"))
```
```{r, add clustering results to subset immune final after stroma merge, message=F, warning=F, echo=F, eval=F}
rp_df <- data.frame("CellID"=immune.final$CellID, "rp_immune_final_k15"=immune.final$rp_immune_final_k15,
"rp_immune_final_k20"=immune.final$rp_immune_final_k20,
"rp_immune_final_k30"=immune.final$rp_immune_final_k30,
"som_25"=immune.final$som_25)
cur_DF <- as_tibble(colData(immune.final.sub)) %>% left_join(rp_df, by = "CellID") %>% DataFrame()
all.equal(paste(cur_DF$ImageNumber, cur_DF$CellNumber), paste(immune.final.sub$ImageNumber, immune.final.sub$CellNumber))
colData(immune.final.sub) <- cur_DF
rownames(colData(immune.final.sub)) <-immune.final.sub$CellID
```
```{r, cluster immune cells using FLOWSOM, echo=F, eval=FALSE, warning=F, message=F, fig.width=15, fig.height=10, eval=F}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))
assay(immune.final, "exprs")<-assay(immune.final, "c_counts_asinh")
#run FlowSOM
re <- CATALYST::cluster(immune.final, features =immune.marker, verbose = FALSE, maxK = 40)
cl <-c(25)
#i <-5
for (i in cl){
#i=35
cluster <- paste0("som_",i)
immune.final[[cluster]] <- as.factor(cluster_ids(re, paste0("meta",i)))
agg_sce <- aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled")
#plot Heatmap
scater::plotHeatmap(agg_sce,
#features = fibro.marker,
features=immune.marker,
exprs_values = "c_counts_asinh_scaled",
#symmetric = FALSE,
zlim=c(-0,1),
color=rdylbu,
sortrowss=FALSE,
show_colnames=TRUE,
color_columns_by = c("ids"),
main=paste0("Heatmap immune, ",cluster))
}
saveRDS(immune.final,file=file.path(data_folder, "final_immune_afterStromaMerge_workingfile.rds"))
```
```{r,Plot HM FLOWSOM cluster immune final after stroma merge, echo=F, warning=F, message=F, fig.width=15, fig.height=10}
hmcol<-rev(brewer.pal(11,"RdBu"))
rdylbu <-rev(brewer.pal(11,"RdYlBu"))
cl <-c(25)
#i <-5
for (i in cl){
#i=35
cluster <- paste0("som_",i)
agg_sce <- aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled")
#plot Heatmap
scater::plotHeatmap(agg_sce,
#features = fibro.marker,
features=immune.marker,
exprs_values = "c_counts_asinh_scaled",
#symmetric = FALSE,
zlim=c(-0,1),
color=rdylbu,
sortrowss=FALSE,
show_colnames=TRUE,
color_columns_by = c("ids"),
main=paste0("Heatmap immune, ",cluster))
}
```
```{r, subset immune final into t cell and non immune T cell, message=F, echo=F, warning=F}
is.tcell <- c(3,4,5,8,10)
tcell.sce <- immune.final[, immune.final$som_25 %in% is.tcell]
immune.nontcell <-immune.final[, !immune.final$som_25 %in% is.tcell]
unique(tcell.sce$som_25)
#saveRDS(tcell.sce, file=file.path(data_folder,"Tcell","tcells_RAW.rds"))
#saveRDS(immune.nontcell, file=file.path(data_folder,"IMMUNE_immune_nontcells_RAW.rds"))
```
```{r, after T cell clustering merge all non T cell immune cells, message=F, echo=F, warning=F, eval=F}
#B cells and immune cells from T cell sce
tcell.bcells <- readRDS("~/data/lena_processed2/NSCLC_NEW/sce_objects/immune/Tcell/tcells_bcells.RDS")
tcell.immune <- readRDS("~/data/lena_processed2/NSCLC_NEW/sce_objects/immune/Tcell/tcell-final-immune.rds")
colnames(colData(tcell.immune))[!colnames(colData(tcell.immune)) %in%colnames(colData(immune.nontcell))]
tcell.immune$TcellCategory <-NULL
tcell.immune$TcellType <-NULL
tcell.immune$rp_tcell_tb_k20 <-NULL
tcell.immune$rp_tcell_k20 <-NULL
tcell.immune$rp_tcell_k15 <-NULL
tcell.immune$rp_tcell_k30 <-NULL
tcell.immune$rp_tcell_k35 <-NULL
tcell.bcells$TcellCategory <-NULL
tcell.bcells$TcellType <-NULL
tcell.bcells$rp_tcell_tb_k20 <-NULL
tcell.bcells$rp_tcell_k20 <-NULL
tcell.bcells$rp_tcell_k15 <-NULL
tcell.bcells$rp_tcell_k30 <-NULL
tcell.bcells$rp_tcell_k35 <-NULL
tcell.bcells$rp_tcell_all_k20 <-NULL
tcell.bcells$rp_tcell_immune_k20 <-NULL
colnames(colData(immune.nontcell))[!colnames(colData(immune.nontcell)) %in%colnames(colData(tcell.bcells))]
immune.nontcell$som_30 <-NULL
immune.nontcell$som_1 <-NULL
immune.nontcell$som_2 <-NULL
immune.nontcell$som_3 <-NULL
immune.nontcell$som_4 <-NULL
immune.nontcell$som_5 <-NULL
immune.nontcell$som_6 <-NULL
immune.nontcell$som_7 <-NULL
immune.nontcell$som_8 <-NULL
immune.nontcell$som_9 <-NULL
immune.final <- cbind(immune.nontcell, tcell.bcells, tcell.immune)
saveRDS(immune.final, file=file.path(data_folder,"NonTcell_immune_RAW_afterStroma_Tcell-merge.rds"))
saveRDS(immune.final, file=file.path(data_folder,"NonTcell_immune_afterStroma_Tcell-merge_workingfile.rds"))
```
#####################################################################################################################################
#after remerging the no T cells from the T cell data.
```{r, Set wd and load data non T cell immune, message=F, echo=F, warning=F}
#set working directory
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","immune"))
#RAW
#immune.final <- readRDS(file=file.path(data_folder, "NonTcell_immune_RAW_afterStroma_Tcell-merge.rds"))
#workingfile
immune.final <- readRDS(file=file.path(data_folder, "NonTcell_immune_afterStroma_Tcell-merge_workingfile.rds"))
set.seed(101100)
```
```{r, Define immune markers non T cell immune, echo=F, warning=F, message=FALSE}
all.marker <-rownames(immune.final)
bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3")
good.marker <- all.marker[!all.marker %in% bad.marker]
immune.marker <-c("Myeloperoxidase (MPO)" ,"HLA-DR","CD20","CD68","CD45RA + CD45R0" ,"CD15")
print(immune.marker)
```
```{r, subset non T cell immune, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(immune.final)), immune.final$Tma_ac)
length(unique(immune.final$Tma_ac))
#sample 'n_cells_dr' per ImageNumber
cs <- unlist(lapply(cs, function(u)
sample(u, min(n_cells_dr, length(u)))))
#sub.test <- sce[,cs]
immune.final.sub <- immune.final[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(immune.final.sub))[2]/dim(assay(immune.final))[2]*100
#results in % of all cells
print(paste("Subsetting results in a total number of",dim(assay(
immune.final.sub))[2],"cells, which corresponds to",round(p,digits=2), "% of all cells of the original sce." ))
saveRDS(immune.final.sub, file=file.path(data_folder, paste("non_tcell_immune_sub.rds")))
```
```{r, load subset non t cell immune, message=FALSE, warning=FALSE, echo=FALSE}
immune.final.sub <- readRDS(file=file.path(data_folder, paste("non_tcell_immune_sub.rds")))
colnames(colData(immune.final.sub))
immune.final.sub$rp_immune_final_k15<-NULL
```
```{r, calculate umap non T cell immune, warning=F, message=F, echo=F, eval=FALSE}
p <-c(10,50,100)
p=50
for(i in p){
immune.final.sub <- runUMAP(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("UMAP_p", i),
#use_dimred="PCA_20",
n_neighbors = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("non_tcell_immune_sub.rds")))
}
saveRDS(immune.final.sub, file=file.path(data_folder, paste("non_tcell_immune_sub.rds")))
for(i in p){
immune.final.sub <- runTSNE(immune.final.sub,
exprs_values = "c_counts_asinh",
name = paste0("tSNE_p", i),
#use_dimred="PCA_20",
perplexity = i)
saveRDS(immune.final.sub, file=file.path(data_folder, paste("non_tcell_immune_sub.rds")))
}
```
**UMAP with good markers**
```{r,plot umap non t cell immune marker, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE}
dat <-as.data.frame(reducedDims(immune.final.sub)$`UMAP_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(immune.final.sub,"c_counts_asinh_scaled"))))
dat.counts$cell <- rownames(dat.counts)
dat.all <-merge(dat, dat.counts, by.x="cell", by.y="cell")
dat.all.long <-pivot_longer(dat.all, cols = colnames(dat.all)[colnames(dat.all) %in% good.marker], names_to = "target", values_to = "counts")
p <-dat.all.long %>%
ggplot(aes(x=V1, y=V2, color=counts))+
facet_wrap(~target, scales = "free", ncol = 6)+
geom_point(alpha=0.5, size=0.2)+
scale_color_gradientn(colours=rev(brewer.pal(11, 'Spectral')), name='Counts')+
ggtitle('')+
theme(strip.background = element_blank(),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank(),
strip.text = element_text(size=8))
##ggsave(filename=file.path(plot_folder, paste("sub_fibro_fibro-Marker_UMAP_p50_NEW.png",sep="")), plot=p, width=16, height=10)
plot(p)
```
**tsne with Tumour markers**
```{r,plot tsne immune marker non T cell immune, fig.width=12, fig.height=8, echo=F, message=FALSE,warning=FALSE, eval=F}
dat <-as.data.frame(reducedDims(immune.final.sub)$`tSNE_p50`)
dat$cell <- rownames(dat)
dat.counts <-as.data.frame(t((assay(immune.final.sub,"c_counts_asinh_scaled"))))
dat.counts$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 non T cell immune using Rphenoannoy, fig.width=12, fig.height=3, 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<-50
for (i in k) {
immune.final$RPmembership <- factor(Rphenoannoy(data = t(assay(immune.final[rownames(immune.final) %in% immune.marker,],"c_counts_asinh")),k = i)[[2]]$membership)
cluster <- paste0("rp_immune_k",i)
colnames(colData(immune.final))[which(names(colData(immune.final)) == "RPmembership")] <- paste0(cluster)
agg_sce <-aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = immune.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(immune.final, file=file.path(data_folder, "NonTcell_immune_afterStroma_Tcell-merge_workingfile.rds"))
}
#immune.final <-readRDS( file=file.path(data_folder, "NonTcell_immune_afterStroma_Tcell-merge_workingfile.rds"))
```
```{r, add cell categories non t cell immune, fig.width=12, fig.height=4, message=F, echo=F, warning=F}
is.bcell <- c(25,26,31,21,19,22,20)
is.neutro <- c(17,18,27,32,23)
is.myeloid <-c(11,12,14,28,30,10,16,6,9,24,5,1,13,7)
is.other <-c(29,2,8,3,4,15)
immune.final$immune_category[immune.final$rp_immune_k20 %in% is.bcell]<-"Bcell"
immune.final$immune_category[immune.final$rp_immune_k20 %in% is.neutro]<-"Neutrophil"
immune.final$immune_category[immune.final$rp_immune_k20 %in% is.myeloid]<-"Myeloid"
immune.final$immune_category[immune.final$rp_immune_k20 %in% is.other]<-"other"
cluster<-"rp_immune_k20"
agg_sce <-aggregateAcrossCells(immune.final, ids=immune.final[[cluster]], average=TRUE, use_exprs_values="c_counts_asinh_scaled" )
scater::plotHeatmap(agg_sce,
features = immune.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("immune_category"),
main=paste0("Heatmap tumour cells, ",cluster))
```
```{r, add clustering results to subset no tcell immune, message=F, echo=F, warning=F}
immune.final.sub$rp_immune_k20 <-NULL
immune.final.sub$immune_category <-NULL
rp_df <- data.frame("CellID"=immune.final$CellID,
"rp_immune_k20"=immune.final$rp_immune_k20,
"immune_category"=immune.final$immune_category)
cur_DF <- as_tibble(colData(immune.final.sub)) %>% left_join(rp_df, by = "CellID") %>% DataFrame()
all.equal(paste(cur_DF$ImageNumber, cur_DF$CellNumber), paste(immune.final.sub$ImageNumber, immune.final.sub$CellNumber))
colData(immune.final.sub) <- cur_DF
rownames(colData(immune.final.sub)) <-immune.final.sub$CellID
```
```{r, non t cell immune sub umap clusters, message=F, echo=F, warning=F}
cluster <- "rp_immune_k20"
p <-plotReducedDim(immune.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)
cluster<- "immune_category"
p <-plotReducedDim(immune.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)
```
```{r, non t cell immune sub final mod, message=F, echo=F, warning=F, eval=F}
colnames(colData(immune.final))
immune.final$rp_immune_k30 <- NULL
immune.final$rp_immune_k40<- NULL
immune.final$rp_immune_k50<- NULL
immune.final$rp_immune_k20.1<- NULL
immune.final$rp_immune_final_k15<- NULL
immune.final$rp_immune_final_k20<- NULL
immune.final$rp_immune_final_k30<- NULL
immune.final$rp_immune_final_k10<- NULL
saveRDS(immune.final, file=file.path(data_folder, "NonTcell_immune_afterStroma_Tcell-merge_workingfile.rds"))
```
#merge non t cell immune and t cell
```{r, merge non t cell immune and t cell sce after clustering back, message=F, echo=F, warning=F, eval=F}
tcell.final <- readRDS("~/data/lena_processed2/NSCLC_NEW/sce_objects/immune/Tcell/FINAL_Tcells-minusImmune_workingfile.rds")
tcell.final$immune_category <-"Tcell"
immune.final$TcellCategory <-immune.final$immune_category
immune.final$TcellType<-immune.final$immune_category
immune.final$rp_immune_k20 <-NULL
tcell.final$rp_immune_final_k10 <-NULL
tcell.final$rp_immune_final_k15 <-NULL
tcell.final$rp_immune_final_k20 <-NULL
tcell.final$rp_tcell_k15 <-NULL
tcell.final$rp_tcell_k20 <-NULL
tcell.final$rp_tcell_k30 <-NULL
tcell.final$rp_tcell_k35 <-NULL
tcell.final$rp_tcell_tb_k20 <-NULL
tcell.final$rp_immune_final_k30<-NULL
colnames(colData(immune.final))[!colnames(colData(immune.final)) %in%colnames(colData(tcell.final))]
colnames(colData(tcell.final))[!colnames(colData(tcell.final)) %in%colnames(colData(immune.final))]
all.immune.cells <-cbind(immune.final, tcell.final)
#saveRDS(all.immune.cells, file=file.path(data_folder, "FINAL_ALL_IMMUNE_CELLS_incTcellTypes.RDS"))
```
#############################################################################################################################################################################
#All immune cells including T cell types
```{r, Set wd and load data all immune combined, message=F, echo=F, warning=F}
#set working directory
wd <-dirname(getwd())
data_folder <-(file.path(wd,"sce_objects","immune"))
#RAW
#all.immune.cells <- readRDS(file=file.path(data_folder, "FINAL_ALL_IMMUNE_CELLS_incTcellTypes.RDS"))
#workingfile
all.immune.cells <- readRDS(file=file.path(data_folder, "FINAL_ALL_IMMUNE_CELLS_incTcellTypes_workingfile.RDS"))
#saveRDS(all.immune.cells, file=file.path(data_folder, "FINAL_ALL_IMMUNE_CELLS_incTcellTypes_workingfile.RDS"))
```
```{r, Define immune markers all immune, echo=F, warning=F, message=FALSE}
all.marker <-rownames(all.immune.cells)
bad.marker <- c("Iridium_191","Iridium_193","Cadherin-6","Histone H3")
good.marker <- all.marker[!all.marker %in% bad.marker]
immune.marker <-c("Myeloperoxidase (MPO)" ,"HLA-DR","CD20","CD68","Indoleamine 2- 3-dioxygenase (IDO)","CD3" ,"TCF1/TCF7" ,"FOXP3","CD45RA + CD45R0","CD8a","CD4" ,"CD15" ,"Ki-67","CD279 (PD-1)")
print(immune.marker)
```
```{r, subset all immune including T cells, message=F, echo=F, warning=F, eval=F}
#split cells by ImageNumber
n_cells_dr <- 100
cs <- split(seq_len(ncol(all.immune.cells)), all.immune.cells$Tma_ac)
length(unique(all.immune.cells$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.immune.cells.sub <- all.immune.cells[,cs]
#calculate percentage of cells subsetting results in
p<-dim(assay(all.immune.cells.sub))[2]/dim(assay(all.immune.cells))[2]*100
#results in % of all cells