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subcluster.R
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source('Codes/Functions.R')
Initialize()
#merged_samples <- readRDS('~/RatLiver/Objects/merged_samples_newSamples.rds')
merged_samples <- readRDS('Objects/merged_samples_newSamples_MT-removed.rds')
cell_types <- 'Immune cells'#'endothelial cells' #
### visualize the selected population
endothelial_clusters <- c(4, 6, 16)
Immune_clusters <- c(7, 8, 10, 11, 12, 13, 17)
cluster_name = as.character(Immune_clusters) #Immune_clusters
merged_samples$final_cluster = as.character(merged_samples$res.0.6)
umap_df <- data.frame(Embeddings(merged_samples, 'umap'))
umap_df$clusters = merged_samples$final_cluster
colnames(umap_df)[1:2] = c('UMAP_1', 'UMAP_2')
umap_df$cluster_interest <- ifelse(umap_df$clusters %in% cluster_name,umap_df$clusters , 'other')
ggplot(umap_df,aes(x= UMAP_1, y= UMAP_2, color=clusters))+geom_point()+theme_classic()+ggtitle('all clusters')
ggplot(umap_df,aes(x= UMAP_1, y= UMAP_2, color=cluster_interest))+geom_point()+theme_classic()+ggtitle(cell_types)
gene_name <- 'Ptprc'
umap_df$gene <- GetAssayData(merged_samples)[gene_name,]
ggplot(umap_df,aes(x= UMAP_1, y= UMAP_2, color=gene))+geom_point()+theme_classic()+
ggtitle('Immune cells')+scale_color_viridis(direction = -1)
cluster3_sub_df$umi <- rownames(cluster3_sub_df)
head(cluster3_sub_df)
umap_df$subclusters <- ifelse(rownames(umap_df) %in% cluster3_sub_df$umi ,cluster3_sub_df$subcluster , 'other')
ggplot(umap_df,aes(x= UMAP_1, y= UMAP_2, color=subclusters))+geom_point()+
theme_classic()+ggtitle(cluster_name)
#### subclustering set1 mesenchymal cells ####
old_data_scClustViz_object <- "Results/old_samples/for_scClustViz_mergedOldSamples_mt40_lib1500_MTremoved.RData"
load(old_data_scClustViz_object)
#### the new immune subcluster data ####
merged_samples <- your_scRNAseq_data_object
merged_samples$final_cluster = as.character(sCVdata_list$res.0.6@Clusters)
cluster_name = c('7', '14')
merged_samples$sample_name = ifelse(merged_samples$orig.ident=='rat_DA_01_reseq', 'DA-1',
ifelse(merged_samples$orig.ident=='rat_DA_M_10WK_003', 'DA-2',
ifelse(merged_samples$orig.ident=='rat_Lew_01', 'Lew-1', 'Lew-2')))
### constructing a subset the input seurat object
UMIs <- colnames(merged_samples[,merged_samples$final_cluster %in% cluster_name])
seur = merged_samples[,colnames(merged_samples) %in% UMIs]
seur@assays$SCT <- NULL
seur <- SetAssayData(
object = seur,
assay.type = 'RNA',
new.data = GetAssayData(seur)[,colnames(seur) %in% UMIs],
slot = 'data'
)
colnames([email protected])
[email protected] <- [email protected][,colnames([email protected]) %in% c("orig.ident","nCount_RNA","nFeature_RNA","mito_perc",
"nCount_SCT","nFeature_SCT","cell_type","sample_type",
"strain_type", 'sample_name', 'strain', 'final_cluster')]
seur@reductions$pca <- NULL
seur@reductions$tsne <- NULL
seur@reductions$umap <- NULL
seur@reductions$harmony <- NULL
seur <- SCTransform(seur,conserve.memory=F,verbose=T,
return.only.var.genes=F,variable.features.n = nrow(seur))
DefaultAssay(seur) = 'SCT'
seur <- RunPCA(seur,verbose=T, features=rownames(seur))
## PCA
plot(100 * seur@reductions$pca@stdev^2 / seur@reductions$pca@misc$total.variance,
pch=20,xlab="Principal Component",ylab="% variance explained",log="y")
PC_NUMBER = 20
PC_NUMBER = 15
### Harmony
#seur <- RunHarmony(seur, "sample_type",assay.use="RNA")
seur <- RunHarmony(seur, "sample_name",assay.use="SCT")
seur <- RunUMAP(seur,dims=1:PC_NUMBER, reduction="harmony")
seur <- RunTSNE(seur,dims=1:PC_NUMBER, reduction="harmony")
max_seurat_resolution <- 2.5 ## change this to higher values
FDRthresh <- 0.01 # FDR threshold for statistical tests
min_num_DE <- 10
seurat_resolution <- 0 # Starting resolution is this plus the jump value below.
seurat_resolution_jump <- 0.2
DefaultAssay(seur) = 'SCT' #'RNA' # why? shouldn't the data be normalized first?
seur <- FindNeighbors(seur,reduction="harmony",dims=1:PC_NUMBER,verbose=F)
sCVdata_list <- list()
DE_bw_clust <- TRUE
while(DE_bw_clust) {
if (seurat_resolution >= max_seurat_resolution) { break }
seurat_resolution <- seurat_resolution + seurat_resolution_jump
# ^ iteratively incrementing resolution parameter
seur <- FindClusters(seur,resolution=seurat_resolution,verbose=F)
message(" ")
message("------------------------------------------------------")
message(paste0("-------- res.",seurat_resolution," with ",
length(levels(Idents(seur)))," clusters --------"))
message("------------------------------------------------------")
if (length(levels(Idents(seur))) <= 1) {
message("Only one cluster found, skipping analysis.")
next
}
# ^ Only one cluster was found, need to bump up the resolution!
if (length(sCVdata_list) >= 1) {
temp_cl <- length(levels(Clusters(sCVdata_list[[length(sCVdata_list)]])))
if (temp_cl == length(levels(Idents(seur)))) {
temp_cli <- length(levels(interaction(
Clusters(sCVdata_list[[length(sCVdata_list)]]),
Idents(seur),
drop=T
)))
if (temp_cli == length(levels(Idents(seur)))) {
message("Clusters unchanged from previous, skipping analysis.")
next
}
}
}
curr_sCVdata <- CalcSCV(
inD=seur,
assayType="RNA",
assaySlot="counts",
cl=Idents(seur),
# ^ your most recent clustering results get stored in the Seurat "ident" slot
exponent=NA,
# ^ going to use the corrected counts from SCTransform
pseudocount=NA,
DRthresh=0.1,
DRforClust="harmony",
calcSil=T,
calcDEvsRest=T,
calcDEcombn=T
)
DE_bw_NN <- sapply(DEneighb(curr_sCVdata,FDRthresh),nrow)
# ^ counts # of DE genes between neighbouring clusters at your selected FDR threshold
message(paste("Number of DE genes between nearest neighbours:",min(DE_bw_NN)))
if (min(DE_bw_NN) < min_num_DE) { DE_bw_clust <- FALSE }
# ^ If no DE genes between nearest neighbours, don't loop again.
sCVdata_list[[paste0("res.",seurat_resolution)]] <- curr_sCVdata
}
saveRDS(seur, 'Results/new_samples/Immune_subclusters_c17Included.rds')
saveRDS(seur, 'Results/old_samples/Mesenchymal_subclusters.rds')
saveRDS(sCVdata_list, 'Results/old_samples/Mesenchymal_subclusters_sCVdata_list.rds')
new_data_scCLustViz_object_Immune <- "Results/new_samples/scClustVizObj/for_scClustViz_newSamples_MTremoved_ImmuneSub_c17Included.RData"
save(seur,sCVdata_list,file=new_data_scCLustViz_object_Immune)
# [email protected] <- cbind([email protected], data.frame(lapply(sCVdata_list, function(x) x@Clusters)))
sCVdata_list$res.0.6@Clusters
umap_df$umi = rownames(umap_df)
subcluster_df <- data.frame(subclust=sCVdata_list$res.0.6@Clusters,
umi=names(sCVdata_list$res.0.6@Clusters))
seur_2 <- readRDS('Results/new_samples/Immune_subclusters.rds') ### Immune_ , endothelial_
seur_umap <- data.frame(getEmb(seur,'umap'), cluster=as.character(seur$SCT_snn_res.1))
seur_umap <- data.frame(getEmb(seur,'umap'), cluster=as.character(seur$final_cluster))
ggplot(seur_umap, aes(UMAP_1, UMAP_2, color=cluster))+geom_point()+theme_classic()+scale_color_manual(values=colorPalatte)
head(subcluster_df)
head(umap_df)
umap_df <- merge(umap_df, subcluster_df, by.x='umi', by.y='umi', all.x=T)
head(umap_df)
ggplot(umap_df,aes(x= UMAP_1, y= UMAP_2, color=subclust))+geom_point()+
theme_classic()+ggtitle(cluster_name)
markers_df_list = lapply(sCVdata_list$res.0.6@DEvsRest, function(markers_df){
markers_df_2 <- markers_df[order(markers_df$logGER*(-log10(markers_df$FDR)),decreasing = T),]
Rp_genes_index <- grep(pattern = 'Rp', x = rownames(markers_df_2))
Mt_genes_index <- grep(pattern = 'Mt-', x = rownames(markers_df_2))
markers_df_2 <- markers_df_2[-c(Rp_genes_index, Mt_genes_index),]
return(markers_df_2)})
names(markers_df_list) = paste0('subcluster_', names(markers_df_list))
lapply(markers_df_list, head, 15)
merged_samples_2 <- merged_samples
subcluster.df.total = data.frame(umi=colnames(merged_samples_2) ,cluster=Idents(merged_samples_2))
subcluster.df.oneClust = data.frame(umi=names(sCVdata_list$res.0.56@Clusters) ,
cluster=paste0('subcluster_', as.character(sCVdata_list$res.0.56@Clusters)))
subcluster.df.total <- merge(subcluster.df.total, subcluster.df.oneClust, by.x='umi', by.y='umi', all.x=T)
colnames(subcluster.df.total)[2:3] = c('cluster', 'subcluster')
head(subcluster.df.total)
is_other_cluster <- is.na(subcluster.df.total$subcluster)
subcluster.df.total$subcluster[is_other_cluster] <- as.character(subcluster.df.total$cluster[is_other_cluster])
head(subcluster.df.total)
Idents(merged_samples_2) = subcluster.df.total$subcluster
subcluster_index = 5
features <- unique(as.character(rownames(markers_df_list[[subcluster_index]])[1:60]))
DotPlot(merged_samples_2, features = features) + RotatedAxis() +
ggtitle(paste0(cluster_name, ' ', names(markers_df_list)[subcluster_index], ' markers'))
dir.create('~/RatLiver/Results/subclusters')
dir.create('~/RatLiver/Results/subclusters/new_samples/')
saveRDS(sCVdata_list, paste0('~/RatLiver/Results/subclusters/new_samples/',cluster_name,'_subclust.rds'))
dir.create('~/XSpecies/objects/merged_subclusters')
saveRDS(sCVdata_list, '~/XSpecies/objects/merged_subclusters/cluster_3_subclust.rds')
sCVdata_list <- readRDS('~/XSpecies/objects/merged_subclusters/cluster_3_subclust.rds')
lapply(sCVdata_list$res.0.02@Clusters, head)
cluster3_sub_df <- data.frame(subcluster=sCVdata_list$res.0.02@Clusters)
head(cluster3_sub_df)
sCVdata_list <- readRDS('~/XSpecies/objects/merged_subclusters/cluster_5_subclust.rds')
table(sCVdata_list$res.0.1@Clusters)
lapply(sCVdata_list$res.0.1@DEvsRest, head)
#### checking the top markers of sub-clusters
seur_genes_df <- mapper
Cluster_markers <- sCVdata_list$res.0.17@DEvsRest
Cluster_markers_merged <- sapply(1:length(Cluster_markers),
function(i){
markers_df <- Cluster_markers[[i]]
markers_df$ensemble_ids = rownames(markers_df)
## merge the ensemble IDs in the dataframe with the HUGO terms
markers_df_merged <- merge(markers_df, seur_genes_df,
by.x='ensemble_ids',
by.y='V1', all.x=T, all.y=F,sort=F)
#markers_df_merged2 <- markers_df_merged[match(markers_df_merged$ensemble_ids, markers_df$ensemble_ids),]
markers_df_merged2 <- markers_df_merged[order(markers_df_merged$logGER*(-log10(markers_df_merged$FDR)),decreasing = T),]
return(markers_df_merged2)
}, simplify = FALSE)
names(Cluster_markers_merged) = names(sCVdata_list$res.0.1@DEvsRest)
lapply(Cluster_markers_merged, function(x)head(x, 20))