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scRNA.Rmd
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scRNA.Rmd
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---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
```{r}
rm(list = ls())
gc()
library(parallel)
library(Seurat)
library(URD)
library(Matrix)
library(clustree)
library(ggplot2)
library(xlsx)
library(ggpubr)
library(msigdbr)
library(fgsea)
library(scatterpie)
library(FSA)
library(rcompanion)
wd = getwd()
dropNA = function(x){
x = x[!is.na(x)]
return(x)
}
flip <- function(x){
x = as.matrix(x)
y = t(apply(x, 2, rev))
rownames(y)=colnames(x)
colnames(y)=rev(rownames(x))
return (y)
}
gexp = function(scRNA = scRNA, gene, reduction = "UMAP"){
if(reduction %in% c("TSNE", "UMAP", "PCA")){
reduction = tolower(reduction)
}
df = data.frame(scale = scRNA@assays$integrated@scale.data[gene,rownames(scRNA[[]])],
counts = scRNA@assays$RNA@counts[gene,rownames(scRNA[[]])],
x = scRNA@reductions[[reduction]]@cell.embeddings[rownames(scRNA[[]]),1],
y = scRNA@reductions[[reduction]]@cell.embeddings[rownames(scRNA[[]]),2])
df$value = df$scale
df$value = df$value + 0.01 - min(df$value[df$counts>0])
df$value[df$counts==0] = 0
p = ggplot(df, aes(x = x, y = y, color = value))+
geom_point() + labs(title = gene, x = paste(reduction, 1, sep = "_"), y = paste(reduction, 2, sep = "_")) +
scale_color_gradientn(name = "Scaled\nexpression\nlevel",
values = c(0, quantile(df$value[df$value>0]))/max(df$value),
colors = alpha(c("gray", rev(hcl.colors(5, "RdYlBu"))),0.5))+
theme_classic()
return(p)
}
```
```{r}
wd = getwd()
setwd(paste0(wd, "/dt"))
load("res_epi.Rdata")
```
```{r}
setwd(paste0(wd, "/dt"))
markers = read.xlsx(file = "Gene_markers.xlsx", sheetName = "Sheet1")
markers$gene = toupper(markers$gene)
#markers = markers[markers$ofi=="yes",]
el = as.character(markers$group)
el = sapply(el, function(x){
paste(toupper(substr(x,1,1)), substr(x,2,nchar(x)), sep = "")
})
el = sapply(el, function(x){
if(nchar(x)>25){
x = paste(substr(x, 1, which(strsplit(x, "")[[1]]==" ")[which.min(abs((2*which(strsplit(x, "")[[1]]==" ")/nchar(x))-1))]-1),substr(x, which(strsplit(x, "")[[1]]==" ")[which.min(abs((2*which(strsplit(x, "")[[1]]==" ")/nchar(x))-1))]+1, nchar(x)), sep = "\n")
}
return(x)
})
markers$group = as.factor(el)
#markers
```
```{r}
setwd(paste0(wd, "/dt"))
dsigdb = gmtPathways("dsigdb_D1.gmt")
dsigdb = lapply(dsigdb, function(x){x = intersect(x, res$hgnc)})
dsigdb = dsigdb[sapply(dsigdb, length)>0]
setwd(paste0(wd, "/dt"))
aed = read.xlsx("AED.xlsx", sheetName = "Sheet1")
names(dsigdb) = tolower(names(dsigdb))
dsigdb = dsigdb[tolower(names(dsigdb)) %in% gsub("-", " ", tolower(aed$Pharmaceutical))]
```
```{r}
setwd(paste0(wd, "/dt"))
drh = read.xlsx("repurposing_drugs_20200324.xlsx", sheetName = "repurposing_drugs_20200324")
drh = drh[!is.na(drh$target),]
drh = setNames(strsplit(drh$target, "\\|"), drh$pert_iname)
drh = lapply(drh, function(x){x = intersect(x, res$hgnc)})
drh = drh[sapply(drh, length)>0]
names(drh) = tolower(gsub("\\-", " ", names(drh)))
drh = drh[tolower(gsub(" ", "-", names(drh))) %in% tolower(aed$Pharmaceutical)]
```
```{r}
c3 = msigdbr(species = "Homo sapiens", category = "C3", subcategory = "TFT:TFT_Legacy")
c3 = c3 %>% split(x = .$gene_symbol, f = .$gs_name)
c2 = msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CGP")
c2 = c2 %>% split(x = .$gene_symbol, f = .$gs_name)
setwd(paste0(wd, "/dt"))
enc = gmtPathways("gene_set_library_crisp.gmt")
ig = unique(c("REST", res[!res$gr=="nc","hgnc"], enc$REST, unlist(c2[grepl("VERHAAK_GLIOBLASTOMA", names(c2))]), c3$NRSF_01, markers$gene, cc.genes.updated.2019$s.genes, cc.genes.updated.2019$g2m.genes, unlist(dsigdb), unlist(drh)))
#ig
```
```{r}
setwd(paste0(wd, "/scRNA_res"))
m = list()
m[["CE_EPILEPSY_UP"]] = res[res$gr == "Upregulated","hgnc"]
m[["CE_EPILEPSY_DN"]] = res[res$gr == "Downregulated","hgnc"]
m[["REST"]] = c3[["NRSF_01"]]
for(el in unique(dropNA(markers$Big_group))){
m[[el]] = as.character(dropNA(markers[markers$Big_group == el,]$gene))
}
m = c(m, c2[grepl("LEIN_", names(c2))])
m = m[!grepl("KLEIN", names(m))]
m = c(m, c2[c("BHATTACHARYA_EMBRYONIC_STEM_CELL", "BOQUEST_STEM_CELL_DN", "BOQUEST_STEM_CELL_UP", "CONRAD_GERMLINE_STEM_CELL", "CONRAD_STEM_CELL", "JINESH_BLEBBISHIELD_TRANSFORMED_STEM_CELL_SPHERES_DN", "JINESH_BLEBBISHIELD_TRANSFORMED_STEM_CELL_SPHERES_UP", "LEE_NEURAL_CREST_STEM_CELL_DN", "LEE_NEURAL_CREST_STEM_CELL_UP", "WONG_ADULT_TISSUE_STEM_MODULE", "WONG_EMBRYONIC_STEM_CELL_CORE", names(c2[grepl("glio", tolower(names(c2)))]))])
m$codel1p19q = dropNA(c(res[grepl("q", tolower(res$band)) & res$chr == 19,"hgnc"], res[grepl("p", tolower(res$band)) & res$chr == 1,"hgnc"]))
```
```{r}
### Prerequisites
```
```{r}
setwd(paste0(wd, "/dt"))
load(file = "GSE70630_SCT.Rdata")
load(file = "GSE131928_smartseq2_SCT.Rdata")
load(file = "GSE89567_SCT.Rdata")
'load(file = "GSE138794_SCT.Rdata")
GSE138794 = GSE138794[!names(GSE138794)=="GSM4119533"]
GSE138794 = GSE138794[!names(GSE138794)=="GSM4119531"]'
#load(file = "GSE84465.Rdata")
scRNA.assays = c(GSE70630, GSE89567, GSE131928) #, GSE138794 GSE84465
scRNA.assays
```
```{r}
x = list()
for(X in names(scRNA.assays)){
el = scRNA.assays[[X]]
x[[X]] = rowSums(el@assays$RNA@counts>0) / dim(el@assays$RNA@counts)[2]
}
x = sapply(x, function(el){
el[unique(unlist(sapply(x, names)))]
})
x[is.na(x)] = 0
ig = intersect(ig, names(rowMeans(x)[rowMeans(x)>0.1]))
ig
setwd(paste0(wd, "/scRNA_res"))
save(ig, file = "ig.Rdata")
#save(scRNA.assays, ig, file = "script_data.Rdata")
#setwd(paste0(wd, "/scRNA_res"))
#load("ig.Rdata")
```
```{r}
lapply(scRNA.assays, function(el){
table(el$prim_ident)
})
```
```{r}
memory.limit(10*16257)
options(future.globals.maxSize = 8*500*1024^2)
rm(list = c("GSE70630", "GSE89567", "GSE131928"))
EL <- SelectIntegrationFeatures(object.list = scRNA.assays, nfeatures = 3000)
scRNA <- PrepSCTIntegration(object.list = scRNA.assays, anchor.features = EL)
rm(list = c("scRNA.assays"))
scRNA <- FindIntegrationAnchors(object.list = scRNA, normalization.method = "SCT", anchor.features = EL, reference = which(names(scRNA) == "MGH54"), dims = 1:30, reduction = "rpca", k.anchor = 15)
scRNA <- IntegrateData(anchorset = scRNA, normalization.method = "SCT", features.to.integrate = unique(c(scRNA@anchor.features, ig)))
setwd(paste0(wd, "/scRNA_res"))
save(scRNA, file = "integrated_3_SCT_RPCA.Rdata")
```
```{r}
setwd(paste0(wd, "/scRNA_res"))
#c2 = msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CGP")
#c2 = c2 %>% split(x = .$gene_symbol, f = .$gs_name)
m = list()
m[["CE_EPILEPSY_UP"]] = res[res$gr == "Upregulated","hgnc"]
m[["CE_EPILEPSY_DN"]] = res[res$gr == "Downregulated","hgnc"]
m[["REST"]] = c3[["NRSF_01"]]
for(el in unique(dropNA(markers$Big_group))){
m[[el]] = as.character(dropNA(markers[markers$Big_group == el,]$gene))
}
m = c(m, c2[grepl("LEIN_", names(c2))])
m = m[!grepl("KLEIN", names(m))]
m = c(m, c2[c("BHATTACHARYA_EMBRYONIC_STEM_CELL", "BOQUEST_STEM_CELL_DN", "BOQUEST_STEM_CELL_UP", "CONRAD_GERMLINE_STEM_CELL", "CONRAD_STEM_CELL", "JINESH_BLEBBISHIELD_TRANSFORMED_STEM_CELL_SPHERES_DN", "JINESH_BLEBBISHIELD_TRANSFORMED_STEM_CELL_SPHERES_UP", "LEE_NEURAL_CREST_STEM_CELL_DN", "LEE_NEURAL_CREST_STEM_CELL_UP", "WONG_ADULT_TISSUE_STEM_MODULE", "WONG_EMBRYONIC_STEM_CELL_CORE", names(c2[grepl("glio", tolower(names(c2)))]))])
m$codel1p19q = dropNA(c(res[grepl("q", tolower(res$band)) & res$chr == 19,"hgnc"], res[grepl("p", tolower(res$band)) & res$chr == 1,"hgnc"]))
#m = lapply(m, function(el){return(el[el %in% cells_rankings@NAMES])})
```
```{r}
setwd(paste0(wd, "/scRNA_res"))
load(file = "integrated_3_SCT_RPCA.Rdata")
```
```{r}
'setwd(paste0(wd, "/dt"))
x = read.csv("markers_nature-2021.csv")
n = list()
for(el in colnames(x)){
n[[el]] = x[,el][!x[,el] == ""]
n[[el]] = intersect(n[[el]], rownames(scRNA@assays$RNA))
}'
```
```{r}
meta = data.frame(
cell = c("MGH36", "MGH53", "MGH54", "MGH60",
"MGH42", "MGH43", "MGH44", "MGH45", "MGH56", "MGH57", "MGH61", "MGH64", "MGH103", "MGH107pos","MGH107neg",
"MGH66", "MGH100", "MGH101", "MGH102", "MGH104", "MGH105", "MGH106", "MGH110", "MGH113", "MGH115", "MGH121", "MGH122", "MGH124", "MGH125", "MGH126", "MGH128", "MGH129", "MGH136", "MGH143", "MGH151", "MGH152",
"GSM4119531", "GSM4119532", "GSM4119533" , "GSM4119534", "GSM4119535", "GSM4119536", "GSM4119537", "GSM4119538", "GSM4119539",
"BT_S2", "BT_S1", "BT_S4", "BT_S6"), #SF11979 SF11977 SF11956 SF11644 SF11136 SF12017 SF11964 SF11949 SF11612
experiment = c("GSE70630", "GSE70630", "GSE70630","GSE70630",
"GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567", "GSE89567",
rep("GSE131928", times = 21),
"GSE138794", "GSE138794", "GSE138794", "GSE138794", "GSE138794", "GSE138794", "GSE138794", "GSE138794", "GSE138794",
"GSE84465", "GSE84465", "GSE84465", "GSE84465"),
IDH = c("mut", "mut", "mut", "mut",
"mut", "mut", "mut", "mut","mut", "mut", "mut", "mut","mut", "mut", "mut",
rep("wt", times = 21),
"wt","wt","wt","wt","mut","mut","mut","mut","mut",
"wt", "wt", "wt", "wt"),
histology = c("oligodendroglioma","oligodendroglioma","oligodendroglioma","oligodendroglioma",
"astrocytoma","astrocytoma","astrocytoma","GBM","astrocytoma","GBM","astrocytoma","astrocytoma","astrocytoma","astrocytoma","astrocytoma",
rep("GMB", times = 21),
"GBM", "GBM", "GBM", "GBM", "astrocytoma", "astrocytoma", "GBM", "oligodendroglioma", "oligodendroglioma",
"GBM", "GBM", "GBM", "GBM"),
grade = c(2,2,2,2,
3,3,3,4,3,4,3,3,3,2,2,
rep(4, times = 21),
4,4,4,4,2,2,4,3,NA,
4, 4, 4, 4),
recurrent = c("no","no","no","no",
"no", "yes","no","yes","no","no","no","no","no","no","no",
rep("no", times = 21),
"no","no","no","no","no","no", "no","no","yes",
"no", "no", "no", "no"),
age = c(NA,NA,NA,NA,
47,45,32,32,31,42,24,40,26,47,47,
67, 71, 52, 65, 65, 67, 66, 61, 65, 69, 63, 63, 68, 76, 65, 65, 78, 71, 62, 65, 52,
76, 61, 63, 57, 34, 44, 64, 40, 67,
54, 55, 60, 48),
gender = c(NA,NA,NA,NA,
"female", "male", "male", "female", "male", "male", "male", "female", "female", "female","female",
"male", "female", "male", "male", "female",
"female", "male", "male", "male", "female", "male", "male", "female", "male", "male", "female", "male", "male", "male", "male", "male",
"female", "female","male","male","male","male","male","male","male",
NA, NA, NA, NA)
)
meta
for(el in colnames(meta)){
scRNA[[el]] = meta[match(scRNA[[]]$orig.ident, meta$cell),el]
}
scRNA$cell = gsub("pos", "", gsub("neg", "", scRNA[[]]$cell))
unique(scRNA$cell)
```
```{r}
#FULL_SET
```
```{r}
dim = 200
x = VariableFeatures(scRNA)
DefaultAssay(scRNA) = "SCT"
VariableFeatures(scRNA) = x
#VariableFeatures()
scRNA = RunPCA(scRNA, assay = "SCT", features = VariableFeatures(scRNA), reduction.name = "PCA_RNA", npcs = dim)
x = pcaMarchenkoPastur(M = length(VariableFeatures(scRNA)), N = dim(scRNA)[2], pca.sdev = scRNA@reductions$PCA_RNA@stdev, factor = 1)
sum(!x)
dims = (1:dim)[x]
scRNA = RunUMAP(scRNA, reduction = "PCA_RNA", assay = "SCT", dims = dims, reduction.name = "UMAP_RNA")
```
```{r}
scRNA <- FindNeighbors(scRNA, dims = dims, assay = "SCT", reduction = "PCA_RNA")
for(el in 1:20/10){
scRNA <- FindClusters(scRNA, resolution = el, assay = "SCT", reduction = "PCA_RNA")
}
el="SCT_snn_res.0.5"
DimPlot(scRNA,group.by = el, pt.size = 1, reduction = "UMAP_RNA", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
p = list()
for(el in c("CD24", "PTPRC", "MBP", "CLU", "SNAP25", "SCG2", "ACTA2", "APOLD1", "GPR17")){
if(el %in% rownames(scRNA@assays$integrated@data)){
p[[el]] = gexp(scRNA = scRNA, gene = el, reduction = "UMAP_RNA")
}
}
p
```
```{r}
el="SCT_snn_res.0.5"
DimPlot(scRNA,group.by = el, pt.size = 1, reduction = "UMAP_RNA", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
x = xtabs(~SCT_snn_res.0.5 + prim_ident, data = scRNA[[]])
x = x/rowSums(x) > 0.6
X = setNames(rep("unknown", times = length(unique(scRNA$SCT_snn_res.0.5))), unique(scRNA$SCT_snn_res.0.5))
for(el in names(X)){
if(length(colnames(x)[x[el,]])>0){
X[el] = colnames(x)[x[el,]]
}
}
scRNA$dom_clust = X[as.character(scRNA$SCT_snn_res.0.5)]
el="dom_clust"
DimPlot(scRNA,group.by = el, pt.size = 1, reduction = "UMAP_RNA", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
'setwd(paste0(wd, "/scRNA_res"))
df = scRNA[[]]
for(el in dropNA(unique(scRNA$prim_ident))){
df[,el] = ifelse(df$prim_ident == el, 1, 0)
p = clustree(df, prefix = "integrated_snn_res.", node_colour = el, node_colour_aggr = "mean") + scale_color_gradientn(colors = rev(hcl.colors(9, "heat"))) + ggtitle(el)
ggsave(p, filename = paste0("Clustree_", el, ".png"), width = 10, height = 8, dpi = 400)
}'
```
```{r}
Idents(scRNA) = scRNA$SCT_snn_res.0.5
setwd(paste0(wd, "/scRNA_res"))
x = markers
x = x[x$gene %in% rownames(scRNA),]
x = x[!grepl("MDSC", toupper(x$group)),]
p = DotPlot(scRNA, features = unique(as.character(x$gene)), assay = "SCT", cols = "RdYlBu")
p$data$group = factor(x$group[match(p$data$features.plot, x$gene)], levels = unique(x$group))
p = p + facet_grid(. ~ group, drop = T, scales = "free_x", space = "free_x") +theme(strip.text.x = element_text(angle=90), axis.text.x = element_text(angle=90, vjust=1))
ggsave(plot = p, filename = "scRNA_clusters_markers.png", width = 16, height = 16)
```
```{r}
'library(biomaRt)
mart <- useMart("ENSEMBL_MART_ENSEMBL", host = "ensembl.org")
ensembl = useDataset("hsapiens_gene_ensembl",mart=mart)
trl = getBM(attributes=c("ensembl_gene_id","hgnc_symbol", "entrezgene_id", "start_position", "end_position", "chromosome_name", "band", "strand", "description","phenotype_description", "mim_morbid_description"), mart= ensembl)
trl$chromosome_name[nchar(trl$chromosome_name)>3] = gsub("_", "", substr(trl$chromosome_name[nchar(trl$chromosome_name)>3], 10, 11))'
```
```{r}
scRNA$orig.ident = gsub("pos", "", gsub("neg", "", scRNA$orig.ident))
```
```{r}
c("MGH60", "MGH54", "MGH53", "MGH36", "MGH42", "MGH61", "MGH43", "MGH45", "MGH44", "MGH56", "MGH64", "MGH57", "MGH107", "MGH102", "MGH104", "MGH105", "MGH110", "MGH113", "MGH121", "MGH122", "MGH124", "MGH125", "MGH136", "MGH143", "MGH66")
```
```{r}
citation("Seurat")
```
```{r}
'set.seed(2021)
for(smpl in unique(scRNA$orig.ident)){
setwd(paste0(wd, "/scRNA_res/infercnv"))
if (file.exists(smpl)){
setwd(paste0(wd, "/scRNA_res/infercnv/", smpl))
} else {
dir.create(paste0(wd, "/scRNA_res/infercnv/", smpl))
setwd(paste0(wd, "/scRNA_res/infercnv/", smpl))
}
scRNA$sel = ifelse(scRNA$orig.ident == smpl, 1, 0)
sum(scRNA$sel)
if(sum(scRNA$prim_ident[scRNA$sel == 1] == "immune") < 20){
x = table(scRNA[[]][scRNA$experiment == scRNA$experiment[match(smpl, scRNA[[]]$orig.ident)] & scRNA$prim_ident == "immune","orig.ident"])
scRNA$sel[sample((1:length(scRNA$orig.ident))[scRNA$prim_ident == "immune" & scRNA$orig.ident == sample(names(x[x>=20]), 1)], 20)] = 1
}
sum(scRNA$sel)
if(sum(scRNA$prim_ident[scRNA$sel == 1] == "oligodendroglia") < 20){
x = table(scRNA[[]][scRNA$experiment == scRNA$experiment[match(smpl, scRNA[[]]$orig.ident)] & scRNA$prim_ident == "oligodendroglia","orig.ident"])
scRNA$sel[sample((1:length(scRNA$orig.ident))[scRNA$prim_ident == "oligodendroglia" & scRNA$orig.ident == sample(names(x[x>=20]), 1)], 20)] = 1
}
sum(scRNA$sel)
library(infercnv)
infercnv_obj = subset(scRNA, sel == 1)
X = infercnv_obj@assays$RNA@counts
#X = as.sparse(10*((2^(X))-1))
X = X[!is.na(match(rownames(X), trl$hgnc_symbol)),]
x = trl[match(rownames(X), trl$hgnc_symbol),]
x$chr = paste0("chr", x$chromosome_name)
x$x = gsub("X", "23", x$chromosome_name)
x$x = gsub("Y", "24", x$chromosome_name)
x$x = as.numeric(x$x)
x = x[!x$x==0,]
x = x[!is.na(x$x),]
x$x = x$start_position + x$x*max(x$start_position)
x = x[order(x$x, decreasing = F),]
x = x[x$hgnc_symbol %in% rownames(X),]
X = X[rownames(X) %in% x$hgnc_symbol ,]
dim(x)
dim(X)
rownames(x) = x$hgnc_symbol
x = x[,c("chromosome_name", "start_position", "end_position")]
colnames(x) = c("V2", "V3", "V4")
el = paste(ifelse(infercnv_obj$prim_ident == "immune", "immune", ifelse(infercnv_obj$prim_ident == "oligodendroglia", "oligodendroglia", "glioma")), infercnv_obj$orig.ident, sep = "_")
el = data.frame(row.names = rownames(infercnv_obj[[]]), V2 = el)
print(table(el$V2))
library(infercnv)
infercnv_obj = CreateInfercnvObject(
raw_counts_matrix=X,
annotations_file=el,
gene_order_file=x,
ref_group_names= unique(el$V2[!grepl("glioma", el$V2)]))
save(infercnv_obj, file = "infercnv_obj.Rdata")
}'
```
```{r}
'wd = "D:/Files/Main_directory/Nencki/Epilepsy_2020"
library(parallel)
library(infercnv)
for(smpl in rev(c("MGH60", "MGH54", "MGH53", "MGH36", "MGH42", "MGH61", "MGH43", "MGH45", "MGH44", "MGH56", "MGH64", "MGH57", "MGH107", "MGH102", "MGH104", "MGH105", "MGH110", "MGH113", "MGH121", "MGH122", "MGH124", "MGH125", "MGH136", "MGH143", "MGH66"))){
print(smpl)
setwd(paste0(wd, "/scRNA_res/infercnv/", smpl))
load(file = "infercnv_obj.Rdata")
infercnv_obj = infercnv::run(
infercnv_obj,
cutoff=0.75, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir=paste0(wd, "/scRNA_res/infercnv/", smpl),
cluster_by_groups=TRUE,
plot_steps=FALSE,
denoise=TRUE,
HMM=T,
analysis_mode = "subclusters",
tumor_subcluster_partition_method = "qnorm",
no_prelim_plot=TRUE,
HMM_report_by = "cell",
png_res=300
)
save(infercnv_obj, file = "infercnv_res.Rdata")
rm(list = "infercnv_obj")
gc()
}'
```
```{r}
library(infercnv)
wd = getwd()
x = setNames(rep(NA, times = length(colnames(scRNA))), colnames(scRNA))
for(smpl in c("MGH60", "MGH54", "MGH53", "MGH36", "MGH42", "MGH61", "MGH43", "MGH45", "MGH44", "MGH56", "MGH64", "MGH57", "MGH107", "MGH102", "MGH104", "MGH110", "MGH113", "MGH121", "MGH122", "MGH124", "MGH125", "MGH136", "MGH143", "MGH66", "MGH105")){
print(smpl)
setwd(paste0(wd, "/scRNA_res/infercnv/", smpl))
load(file = "infercnv_res.Rdata")
MGH = subset(scRNA, cells = colnames(infercnv_obj@expr.data))
MGH = add_to_seurat(seurat_obj = MGH, infercnv_output_path = paste0(wd, "/scRNA_res/infercnv/", smpl))
smpl = (rowSums(MGH[[]][,grep("top_", colnames(MGH[[]]))])/max(rowSums(MGH[[]][,grep("top_", colnames(MGH[[]]))])))[MGH$orig.ident == smpl]
x[names(smpl)] = smpl
}
x[is.na(x)]
```
```{r}
scRNA$CNA = x[rownames(scRNA[[]])]
FeaturePlot(scRNA, features = "CNA", reduction = "UMAP_RNA")
```
```{r}
DimPlot(scRNA, group.by = "experiment", reduction = "UMAP_RNA")
```
```{r}
p = list()
#p[["clusters"]] = DimPlot(scRNA,group.by = "identity", pt.size = 1, reduction = "umap", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,"identity"])))), 0.5), na.value = "gray")
for(el in c("CD24", "PTPRC", "MBP", "CLU", "SNAP25", "SCG2", "ACTA2", "APOLD1", "GPR17")){
if(el %in% rownames(scRNA@assays$integrated@data)){
p[[el]] = gexp(scRNA = scRNA, gene = el, reduction = "UMAP_RNA")
}
}
p
```
```{r}
scRNA$identity = ifelse(scRNA@reductions$UMAP_RNA@cell.embeddings[,"UMAP_1"] > (5), "immune", ifelse(scRNA@reductions$UMAP_RNA@cell.embeddings[,"UMAP_2"] > (5) , "oligodendroglia", "glioma"))
el="identity"
DimPlot(scRNA,group.by = el, pt.size = 1, reduction = "UMAP_RNA", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
p = list()
p[["clusters"]] = DimPlot(scRNA,group.by = "identity", pt.size = 1, reduction = "UMAP_RNA", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(scRNA[[]][,"identity"])))), 0.5), na.value = "gray")
for(el in c("CD24", "PTPRC", "MBP", "CLU", "SNAP25", "SCG2", "ACTA2", "APOLD1", "GPR17")){
if(el %in% rownames(scRNA@assays$integrated@data)){
p[[el]] = gexp(scRNA = scRNA, gene = el, reduction = "UMAP_RNA")
}
}
setwd(paste0(wd, "/scRNA_res"))
ggsave(plot = ggarrange(plotlist = p, ncol = 2, nrow = 4),width = 14, height = 20, filename = "identity_markers_expression.png", dpi = 400)
```
```{r}
'f = function(x){
x = (x-min(x))/(max(x) - min(x))
}
DefaultAssay(scRNA) = "AUC_m"
el="CE-EPILEPSY-UP"
FeaturePlot(scRNA,features = el, pt.size = 1, reduction = "umap") + scale_color_gradientn(colors = c("cyan","blue", "gray20", "red", "orange"), values = f(quantile(scRNA@assays$AUC_m@counts[el,])))
el="REST"
FeaturePlot(scRNA,features = el, pt.size = 1, reduction = "umap") + scale_color_gradientn(colors = c("cyan","blue", "gray20", "red", "orange"), values = f(quantile(scRNA@assays$AUC_m@counts[el,])))
DefaultAssay(scRNA) = "integrated"'
```
```{r}
scRNA = AddModuleScore(scRNA, features = m["REST"] , name = "NRSF", ctrl = 100, assay = "SCT")
scRNA = AddModuleScore(scRNA, features = m["CE_EPILEPSY_UP"] , name = "Epilepsy", ctrl = 100, assay = "SCT")
scRNA = AddModuleScore(scRNA, features = m["CE_EPILEPSY_DN"] , name = "Epilepsy_neg", ctrl = 100, assay = "SCT")
```
```{r}
scRNA$GAE = scRNA$Epilepsy1 - scRNA$Epilepsy_neg1
```
```{r}
p = list()
df = data.frame(UMAP1 = scRNA@reductions$UMAP_RNA@cell.embeddings[,"UMAP_1"],
UMAP2 = scRNA@reductions$UMAP_RNA@cell.embeddings[,"UMAP_2"],
identity = scRNA$identity,
#REST = scRNA@assays$AUC_m@counts["REST",],
#Epilepsy = scRNA@assays$AUC_m@counts["CE-EPILEPSY-UP",],
REST = scRNA$NRSF1,
Epilepsy = scRNA$GAE)
```
```{r}
p[["identity"]] = ggplot(data = df, aes(x = UMAP1, y = UMAP2, color = identity)) +
geom_point() + scale_color_manual(name = "Cell\nidentity", values = alpha(c("seashell4", "darkturquoise", "darkgreen", "darkgoldenrod1", "tomato", "darkorchid4"),0.5), breaks = c("glioma", "astrocyte", "oligodendroglia", "neuron", "vascular", "immune"), labels = c("neoplastic", "astrocyte", "oligodendrocyte", "neuron", "vascular", "immune")) + theme_classic() + labs(title = "Identified cellular populations", x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank())
p[["identity"]]
```
```{r}
el
```
```{r}
p[["correlation"]] = ggplot(data = df, mapping = aes(x = REST, y = Epilepsy, color = identity)) +
geom_point() + scale_color_manual(name = "Cell\nidentity", values = alpha(c("seashell4", "darkgreen","darkorchid4"),0.5), breaks = c("glioma", "oligodendroglia", "immune"), labels = lapply(c("glioma", "oligodendroglia", "immune"), function(el){
x = cor.test(df[df$identity == el,]$REST, df[df$identity == el,]$Epilepsy)
return(paste0(el, "\ncoef = ", signif(x$estimate,3), "\np = ", signif(x$p.value,3), "\n"))
})) +
geom_smooth(data = df, mapping = aes(x = REST, y = Epilepsy), inherit.aes = F, size = 1, color = "black", method = lm, linetype = "dashed", se = F) +
theme_classic() + labs(title = "Correlation of Seurat signatures", subtitle = paste0("Pearson correlation: coefficient = ", signif(cor.test(df$REST, df$Epilepsy)$estimate,3), ", p-value = ", signif(cor.test(df$REST, df$Epilepsy)$p.value,3)), x = "REST regulated genes signature", y = "GAE signature")
p[["correlation"]]
```
```{r}
f = function(x){
x = (x-min(x))/(max(x) - min(x))
}
x = setNames(c(quantile(df$REST[df$identity=="glioma"]), min(df$REST), max(df$REST)), c("dodgerblue", "cyan", "gray70", "yellow", "red", "blue4", "magenta4"))
x = x[!duplicated(x)]
x = x[order(x)]
p[["REST"]] = ggplot(data = df, aes(x = UMAP1, y = UMAP2, color = REST)) +
geom_point() + scale_color_gradientn(colors = alpha(names(x),0.5), values = f(x), name = "Seurat\nscore") +theme_classic() + labs(title = "REST regulated genes signature", x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank())
p[["REST"]]
```
```{r}
x = setNames(c(quantile(df$Epilepsy[df$identity=="glioma"]), min(df$Epilepsy), max(df$Epilepsy)), c("dodgerblue", "cyan", "gray70", "yellow", "red", "blue4", "magenta4"))
x = x[!duplicated(x)]
x = x[order(x)]
p[["Epilepsy"]] = ggplot(data = df, aes(x = UMAP1, y = UMAP2, color = Epilepsy)) +
geom_point() + scale_color_gradientn(colors = alpha(names(x),0.5), values = f(x), name = "Seurat\nscore") +theme_classic() + labs(title = "Genes upregulated in epilepsy signature", x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank())
p[["Epilepsy"]]
```
```{r}
fig3.1 = p
setwd(paste0(wd, "/Figures"))
ggsave(ggarrange(plotlist = fig3.1, ncol = 2, nrow = 2), filename = "Fig3.1.png", width = 16, height = 10, dpi = 400)
save(fig3.1, file = "fig3.1.Rdata")
```
```{r}
### Glioma subset
```
```{r}
setwd(paste0(wd, "/scRNA_res"))
load(file = "scRNA_SCT_RPCA_identified.Rdata")
```
```{r}
setwd(paste0(wd, "/scRNA_res"))
load(file = "ig.Rdata")
```
```{r}
Idents(scRNA) = scRNA$identity
glioma = subset(scRNA, idents = "glioma")
rm(list = "scRNA")
gc()
```
```{r}
DefaultAssay(glioma) = "integrated"
glioma = FindVariableFeatures(glioma, nfeatures = 3000)
```
```{r}
dim = 200
glioma = RunPCA(glioma, npcs = dim, features = VariableFeatures(glioma))
x = pcaMarchenkoPastur(M = length(VariableFeatures(glioma)), N = dim(glioma)[2], pca.sdev = glioma@reductions$pca@stdev, factor = 1)
dims = (1:dim)[x]
range(dims)
glioma <- RunUMAP(glioma, dims = dims, reduction = "pca")
```
```{r}
el="Phase"
DimPlot(glioma,group.by = el, pt.size = 1, reduction = "umap", label = F) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(glioma[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
el="experiment"
DimPlot(glioma,group.by = el, pt.size = 1, reduction = "umap", label = F) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(glioma[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
el= "prim_ident"
DimPlot(glioma,group.by = el, pt.size = 1, reduction = "umap", label = F) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(glioma[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
glioma <- FindNeighbors(glioma, dims = dims)
for(el in 1:20/10){
glioma <- FindClusters(glioma, resolution = el)
}
```
```{r}
el="integrated_snn_res.0.5"
DimPlot(glioma,group.by = el, pt.size = 1, reduction = "umap", label = T) + scale_color_manual(values = alpha(rainbow(length(unique(dropNA(glioma[[]][,el])))), 0.5), na.value = "gray")
```
```{r}
p = list()
df = data.frame(UMAP1 = glioma@reductions$UMAP_RNA@cell.embeddings[,1],
UMAP2 = glioma@reductions$UMAP_RNA@cell.embeddings[,2],
identity = glioma$identity,
#REST = glioma@assays$AUC_m@counts["REST",],
#Epilepsy = glioma@assays$AUC_m@counts["CE-EPILEPSY-UP",],
REST = glioma$NRSF1,
Epilepsy = glioma$GAE)
```
```{r}
f = function(x){
x = (x-min(x))/(max(x) - min(x))
}
x = setNames(c(quantile(df$REST[df$identity=="glioma"]), min(df$REST), max(df$REST)), c("dodgerblue", "cyan", "gray70", "yellow", "red", "blue4", "magenta4"))
x = x[!duplicated(x)]
x = x[order(x)]
p[["REST"]] = ggplot(data = df, aes(x = UMAP1, y = UMAP2, color = REST)) +
geom_point() + scale_color_gradientn(colors = alpha(names(x),0.5), values = f(x), name = "Seurat\nscore") +theme_classic() + labs(title = "REST regulated genes signature", x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank())
p[["REST"]]
```
```{r}
x = setNames(c(quantile(df$Epilepsy[df$identity=="glioma"]), min(df$Epilepsy), max(df$Epilepsy)), c("dodgerblue", "cyan", "gray70", "yellow", "red", "blue4", "magenta4"))
x = x[!duplicated(x)]
x = x[order(x)]
p[["Epilepsy"]] = ggplot(data = df, aes(x = UMAP1, y = UMAP2, color = Epilepsy)) +
geom_point() + scale_color_gradientn(colors = alpha(names(x),0.5), values = f(x), name = "Seurat\nscore") +theme_classic() + labs(title = "Genes upregulated in epilepsy signature", x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank())
p[["Epilepsy"]]
```
```{r}
for(el in names(m)[grepl("VERHAAK", names(m))]){
glioma = AddModuleScore(glioma, features = m[el] , name = el, ctrl = 100, assay = "SCT")
}
```
```{r}
x = glioma[[]][,grepl("VERHAAK", colnames(glioma[[]]))]
x$grade = glioma$grade
#x$grade = 4
for(el in rownames(glioma@assays$AUC_m)[grepl("VERHAAK", rownames(glioma@assays$AUC_m))]){
x[,el] = (x[,el] - median(x[x$grade==4,el]))/sd(x[x$grade==4,el])
}
x$type = as.character(apply(as.matrix(x[,1:4]), 1, function(el){
colnames(x)[which.max(el)]
}))
x$type[rowSums(as.matrix(x[,1:4])<0) == 4] = "none"
x$type = gsub("VERHAAK_GLIOBLASTOMA_", "", x$type)
x$type = gsub("1", "", x$type)
x
glioma$Verhaak_p = tolower(x$type)
glioma$Verhaak = glioma$Verhaak_p
#glioma$Verhaak[glioma$integrated_snn_res.0.5 %in% c("3", "5")] = "none"
```
```{r}
```
```{r}
unique(glioma$Verhaak)
```
```{r}
df = data.frame(Verhaak = glioma$Verhaak, REST = glioma$NRSF1, GAE = glioma$GAE)
ggplot(data = df, mapping = aes(x = REST, y = GAE, color = Verhaak)) +
geom_point() +
scale_color_manual(name = "Cells subtype\nby Verhaak et al.", values = alpha(c("green","orange", "blue", "purple", "gray"), 0.5), breaks = c("neural","proneural", "classical", "mesenchymal", "none"), labels = c("neural", "proneural", "classical", "mesenchymal", "none")) +
theme_classic()
```
```{r}
library(scatterpie)
library(ggrepel)
setwd(paste0(wd, "/scRNA_res"))
x = xtabs(~orig.ident + Verhaak, data = glioma[[]])
attr(x, "class") <- NULL
attr(x, "call") <- NULL
x = as.data.frame(x)