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Version_8_11 #2

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93 changes: 93 additions & 0 deletions GSE131907_Treg_test.r
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#======================================================================================================
# 2020-11-30
# use samgsung data to run monocle
#======================================================================================================
library(Biobase)
library(knitr)
library(reshape2)
library(ggplot2)
library(Seurat)
library(monocle)

dat <- readRDS("/public/workspace/lily/Lung2Brain/GSE131907/all_cell.RDS")

#=======================================================================================================
tmp.dat <- subset(dat,cells=which(dat$Cell_subtype%in%c("Naive CD4+ T","Treg","CD4+ Th")))
tmp.dat.f <- subset(tmp.dat,cells=which(tmp.dat$type%in%c("Brian_metastasis","tumor_Lung_early")))


tmp_dat <- FindVariableFeatures(object = tmp.dat.f,nfeatures= 3000)
# scaling
all.genes <- rownames(x = tmp_dat)
tmp_dat <- ScaleData(object = tmp_dat, features = all.genes)


#======================================================================================================
data <- as(as.matrix(tmp_dat@assays$RNA@data), 'sparseMatrix')
pd <- new('AnnotatedDataFrame', data = [email protected])
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
fd <- new('AnnotatedDataFrame', data = fData)
# make a object
cds <- newCellDataSet(data,
phenoData = pd,
featureData = fd,
lowerDetectionLimit = 0.5,
expressionFamily = negbinomial.size())

cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds)
cds <- detectGenes(cds, min_expr = 0.1)

ordering_genes <- VariableFeatures(tmp_dat)
cds <- setOrderingFilter(cds, ordering_genes)
cds <- reduceDimension(cds,method = 'DDRTree')
cds <- orderCells(cds)


plot_cell_trajectory(cds,color_by="Cell_subtype")+scale_colour_manual(values=c("#bff199","#bff199","#70b29c"))+facet_wrap(~type, nrow = 3)

plot_cell_trajectory(cds,color_by="Cell_subtype")+scale_colour_manual(values=c("#bff199","#bff199","#70b29c"))+facet_wrap(~State, nrow = 3)


treg <- pData(cds)[which(cds$State%in%c(4,5)&cds$Cell_subtype=="Treg"),c("Cell_subtype","State")]

# treg.dat <- subset(tmp_dat,cells=which(colnames(tmp_dat)%in%rownames(treg)&tmp_dat$type%in%c("Brian_metastasis","tumor_Lung_early")))
treg.dat <- subset(tmp_dat,cells=which(colnames(tmp_dat)%in%rownames(treg)&tmp_dat$type%in%c("Brian_metastasis")))
treg.dat <- subset(tmp_dat,cells=which(colnames(tmp_dat)%in%rownames(treg)&tmp_dat$type%in%c("tumor_Lung_early")))
treg.dat$State <- treg$State[which(rownames(treg)%in%colnames(treg.dat))]


[email protected] <- as.factor(treg.dat$State)
DefaultAssay(treg.dat) <- "RNA"
geneset_tmp <- FindMarkers(treg.dat,ident.1=4,ident.2=5,assay="RNA",logfc.threshold = 0.1)






























237 changes: 237 additions & 0 deletions Multiple_LB.R
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# 2021-4-6
# run multiple Lung cancer sample in Lung2Brain
# run in 202.195.187.3
# D0927 and E0927
# 季桂枝 省人医
#====================================================================================================
library(Seurat)
# D0927
tmp <- Read10X(data.dir = "/public/workspace/lily/Mutiple_LB/D0927/D0927/outs/filtered_feature_bc_matrix/")
tmp.dat <- CreateSeuratObject(counts = tmp, project = "D0927",min.cells = 3, min.features = 200)

prepare <- function(tmp_dat){
tmp_dat[["percent.mt"]] <- PercentageFeatureSet(object = tmp_dat, pattern = "^MT-")
tmp_dat = subset(x=tmp_dat,subset=nFeature_RNA > 200 & nFeature_RNA < 7500 & percent.mt < 20)
# seurat object
tmp_dat = NormalizeData(object = tmp_dat)
tmp_dat <- FindVariableFeatures(object = tmp_dat)
# scaling
all.genes <- rownames(x = tmp_dat)
tmp_dat <- ScaleData(object = tmp_dat, features = all.genes)
# PCA
tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat))
# clustering
tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10)
# select proper resolution
tmp_dat <- FindClusters(object = tmp_dat,resolution=0.8)
# T-SNE
tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE)
tmp_dat <- RunUMAP(tmp_dat,dims=1:10)
return(tmp_dat)
}
tmp.dat <- prepare(tmp.dat)
dat <- tmp.dat
# use genemarker
pdf(paste0("/public/workspace/lily/Mutiple_LB/D0927_violin.pdf"),width=12,height=9)
DefaultAssay(dat) <- "RNA"
VlnPlot(dat,features=c("CD3D","CD3E","CD2","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # T cells
VlnPlot(dat,features=c("CD19","MS4A1","CD79A","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # B cells
VlnPlot(dat,features=c("CD68","FCGR3A","LYZ","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Myeloid
VlnPlot(dat,features=c("NCAM1","NKG7","CD3D","KLRD1"),assay="RNA",group.by="seurat_clusters",pt.size=0) # NK cells
VlnPlot(dat,features=c("COL1A1","COL1A2","THY1","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Fibroblast
VlnPlot(dat,features=c("PTPRC","CLDN5","FLT1","RAMP2"),assay="RNA",group.by="seurat_clusters",pt.size=0) #Endothelials
VlnPlot(dat,features=c("EPCAM","KRT19","CDH1","EGFR"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Epithelials
VlnPlot(dat,features=c("TPSAB1","TPSB2","MS4A2","KIT"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Mast cells
VlnPlot(dat,features=c("CLEC10A","CD1C", "CLEC4C", "IL3RA"),assay="RNA",group.by="seurat_clusters",pt.size=0) # pDC
dev.off()

pdf(paste0("/public/workspace/lily/Mutiple_LB/D0927_feature.pdf"),width=10,height=10)
DimPlot(dat,label=T)
FeaturePlot(dat,features=c("CD3D","CD3E","CD2","PTPRC"),label=T) # T cells
FeaturePlot(dat,features=c("CD19","MS4A1","CD79A","PTPRC"),label=T) # B cells
FeaturePlot(dat,features=c("CD68","FCGR3A","LYZ","PTPRC"),label=T) # Myeloid
FeaturePlot(dat,features=c("NCAM1","NKG7","CD3D","KLRD1"),label=T) # NK cells
FeaturePlot(dat,features=c("COL1A1","COL1A2","THY1","PTPRC"),label=T) # Fibroblast
FeaturePlot(dat,features=c("PTPRC","CLDN5","FLT1","RAMP2"),label=T) #Endothelials
FeaturePlot(dat,features=c("EPCAM","KRT19","CDH1","EGFR"),label=T) # Epithelials
FeaturePlot(dat,features=c("TPSAB1","TPSB2","MS4A2","KIT"),label=T) # Mast cells
FeaturePlot(dat,features=c("CLEC10A","CD1C","CLEC4C", "IL3RA"),label=T) # pDC
dev.off()

dat$infercnv.type <- "tmp"
dat$infercnv.type[which(dat$seurat_clusters%in%c(2,15))] <- "Tcell"
dat$infercnv.type[which(dat$seurat_clusters%in%c(0,1,4,8,11))] <- "Tumor"
saveRDS(dat,file="/public/workspace/lily/Mutiple_LB/D0927.RDS")


###############################################################################################################################
# run infercnv
library(infercnv)
library(Seurat)
D0927 <- readRDS("/public/workspace/lily/Mutiple_LB/D0927.RDS")
sub.dat <- subset(D0927,cells=which(D0927$infercnv.type%in%c("Tcell","Tumor")))


setwd('/public/workspace/lily/Mutiple_LB/infercnv/')
dir.create('./D0927')
write.table(sub.dat$infercnv.type,file="D0927_cell_info.txt",sep="\t",col.names=F,quote=F)
count<-as.matrix(sub.dat@assays$RNA@counts)
write.table(count,file="D0927_count_exp.txt",sep="\t",quote=F)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix="/public/workspace/lily/Mutiple_LB/infercnv/D0927_count_exp.txt",
annotations_file="/public/workspace/lily/Mutiple_LB/infercnv/D0927_cell_info.txt",
delim="\t",
gene_order_file="/public/workspace/lily/REF/INDEX-hg19/anno/gencode_hg19_pos.txt",
ref_group_names="Tcell")


setwd('/public/workspace/lily/Mutiple_LB/infercnv/D0927')
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir="./",
cluster_by_groups=T,
plot_steps=F,
no_prelim_plot = TRUE,
num_threads=10, #big
no_plot=F ,
output_format = "pdf" # maybe can more quick
# used for final scaling to fit range (0,2) centered at 1.
)






#===========================================================================================================================
# E0927
tmp <- Read10X(data.dir = "/public/workspace/lily/Mutiple_LB/E0927/E0927/outs/filtered_feature_bc_matrix/")
tmp.dat <- CreateSeuratObject(counts = tmp, project = "E0927",min.cells = 3, min.features = 200)

prepare <- function(tmp_dat){
tmp_dat[["percent.mt"]] <- PercentageFeatureSet(object = tmp_dat, pattern = "^MT-")
tmp_dat = subset(x=tmp_dat,subset=nFeature_RNA > 200 & nFeature_RNA < 7500 & percent.mt < 20)
# seurat object
tmp_dat = NormalizeData(object = tmp_dat)
tmp_dat <- FindVariableFeatures(object = tmp_dat)
# scaling
all.genes <- rownames(x = tmp_dat)
tmp_dat <- ScaleData(object = tmp_dat, features = all.genes)
# PCA
tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat))
# clustering
tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10)
# select proper resolution
tmp_dat <- FindClusters(object = tmp_dat,resolution=0.8)
# T-SNE
tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE)
tmp_dat <- RunUMAP(tmp_dat,dims=1:10)
return(tmp_dat)
}
tmp.dat <- prepare(tmp.dat)
dat <- tmp.dat
# use genemarker
pdf(paste0("/public/workspace/lily/Mutiple_LB/E0927_violin.pdf"),width=12,height=9)
DefaultAssay(dat) <- "RNA"
VlnPlot(dat,features=c("CD3D","CD3E","CD2","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # T cells
VlnPlot(dat,features=c("CD19","MS4A1","CD79A","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # B cells
VlnPlot(dat,features=c("CD68","FCGR3A","LYZ","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Myeloid
VlnPlot(dat,features=c("NCAM1","NKG7","CD3D","KLRD1"),assay="RNA",group.by="seurat_clusters",pt.size=0) # NK cells
VlnPlot(dat,features=c("COL1A1","COL1A2","THY1","PTPRC"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Fibroblast
VlnPlot(dat,features=c("PTPRC","CLDN5","FLT1","RAMP2"),assay="RNA",group.by="seurat_clusters",pt.size=0) #Endothelials
VlnPlot(dat,features=c("EPCAM","KRT19","CDH1","EGFR"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Epithelials
VlnPlot(dat,features=c("TPSAB1","TPSB2","MS4A2","KIT"),assay="RNA",group.by="seurat_clusters",pt.size=0) # Mast cells
VlnPlot(dat,features=c("CLEC10A","CD1C", "CLEC4C", "IL3RA"),assay="RNA",group.by="seurat_clusters",pt.size=0) # pDC
dev.off()

pdf(paste0("/public/workspace/lily/Mutiple_LB/E0927_feature.pdf"),width=10,height=10)
DimPlot(dat,label=T)
FeaturePlot(dat,features=c("CD3D","CD3E","CD2","PTPRC"),label=T) # T cells
FeaturePlot(dat,features=c("CD19","MS4A1","CD79A","PTPRC"),label=T) # B cells
FeaturePlot(dat,features=c("CD68","FCGR3A","LYZ","PTPRC"),label=T) # Myeloid
FeaturePlot(dat,features=c("NCAM1","NKG7","CD3D","KLRD1"),label=T) # NK cells
FeaturePlot(dat,features=c("COL1A1","COL1A2","THY1","PTPRC"),label=T) # Fibroblast
FeaturePlot(dat,features=c("PTPRC","CLDN5","FLT1","RAMP2"),label=T) #Endothelials
FeaturePlot(dat,features=c("EPCAM","KRT19","CDH1","EGFR"),label=T) # Epithelials
FeaturePlot(dat,features=c("TPSAB1","TPSB2","MS4A2","KIT"),label=T) # Mast cells
FeaturePlot(dat,features=c("CLEC10A","CD1C","CLEC4C", "IL3RA"),label=T) # pDC
dev.off()

dat$infercnv.type <- "tmp"
dat$infercnv.type[which(dat$seurat_clusters%in%c(0,13,14))] <- "Tcell"
dat$infercnv.type[which(dat$seurat_clusters%in%c(7,9,11,17))] <- "Tumor"
saveRDS(dat,file="/public/workspace/lily/Mutiple_LB/E0927.RDS")




############################################################################################################################
# run infercnv
library(infercnv)
library(Seurat)
E0927 <- readRDS("/public/workspace/lily/Mutiple_LB/E0927.RDS")
sub.dat <- subset(E0927,cells=which(E0927$infercnv.type%in%c("Tcell","Tumor")))


setwd('/public/workspace/lily/Mutiple_LB/infercnv/')
dir.create('./E0927')
write.table(sub.dat$infercnv.type,file="E0927_cell_info.txt",sep="\t",col.names=F,quote=F)
count<-as.matrix(sub.dat@assays$RNA@counts)
write.table(count,file="E0927_count_exp.txt",sep="\t",quote=F)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix="/public/workspace/lily/Mutiple_LB/infercnv/E0927_count_exp.txt",
annotations_file="/public/workspace/lily/Mutiple_LB/infercnv/E0927_cell_info.txt",
delim="\t",
gene_order_file="/public/workspace/lily/REF/INDEX-hg19/anno/gencode_hg19_pos.txt",
ref_group_names="Tcell")


setwd('/public/workspace/lily/Mutiple_LB/infercnv/E0927')
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir="./",
cluster_by_groups=T,
plot_steps=F,
no_prelim_plot = TRUE,
num_threads=10, #big
no_plot=F ,
output_format = "pdf" # maybe can more quick
# used for final scaling to fit range (0,2) centered at 1.
)



































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