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Create CSC_BCSC_analysis-BC_Epithelial_cell_data.R
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| start on 25Feb | ||
| BC_Epithelial_cell_data | ||
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| library(Seurat) | ||
| library(ggplot2) | ||
| library(RColorBrewer) | ||
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| setwd('/storage2/Project/CSC/10X/DGIST_data02') | ||
| DGIST=readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Epithelial_cell_data/EpithelialCells_SeuratSet.rds") | ||
| #BCSC_meta$ = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Epithelial_cell_data/Epithelial_metadata.rds") | ||
| #BCSC_raw = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Epithelial_cell_data/Epithelial_Rawcount.rds") | ||
| #BCSC_norm = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Epithelial_cell_data/Epithelial_normalizedExprs.rds") | ||
| DGIST@assays DGIST@active.ident DGIST@reductions DGIST@version | ||
| DGIST@meta.data DGIST@graphs DGIST@project.name DGIST@commands | ||
| DGIST@active.assay DGIST@neighbors DGIST@misc DGIST@tools | ||
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| pdf("DGIST_data02_BCSC_tsne.pdf") | ||
| DimPlot(DGIST,reduction = "tsne") | ||
| dev.off() | ||
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| #윤정교 교수님 wnt signal | ||
| target = 'RHF43' | ||
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| pdf(paste0("dr.Yoon_wnt_",target,".pdf")) | ||
| cols <- brewer.pal(9,"YlOrRd") | ||
| df = data.frame(x=DGIST@reductions$tsne@cell.embeddings[, "tSNE_1"], | ||
| y=DGIST@reductions$tsne@cell.embeddings[, "tSNE_2"], | ||
| expression=DGIST@assays$RNA@scale.data[target,]) | ||
| ggplot(df,aes(x=x, y=y, colour=expression)) + | ||
| geom_point(size=1) + | ||
| scale_colour_gradientn(colours = cols ) + | ||
| ylab("Component 2") + | ||
| xlab("Component 1") + | ||
| theme_bw() + | ||
| theme(text = element_text(size=20), | ||
| panel.grid.major=element_blank(), | ||
| panel.grid.minor=element_blank(), | ||
| axis.line=element_line(size=1), | ||
| axis.ticks=element_line(size=1), | ||
| legend.text=element_text(size=20), | ||
| legend.title=element_blank(), | ||
| legend.key=element_blank(), | ||
| axis.text.x = element_text(size=20) | ||
| )+ ggtitle(paste0("Wnt signal : ",target)) | ||
| dev.off() | ||
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| #bulkRNA-seq deconvolution 비교 | ||
| #cluster별 variable gene 찾고 CIBERSORT, Xcell에 대입 | ||
| HP_raw = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Hematopoietic_cell_data/Hematopoietic_Rawcount.rds") | ||
| HP_norm = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Hematopoietic_cell_data/Hematopoietic_normalizedExprs.rds") | ||
| HP_meta = readRDS(file="/storage2/Project/CSC/10X/DGIST_data02/BC_Hematopoietic_cell_data/Hematopoietic_metadata.rds") | ||
| table(HP_meta$cell_label_details) | ||
| B_cell Bone_marrow_cells CMP | ||
| 3017 17 10 | ||
| Dendritic Erythroblast GMP | ||
| 4322 6 34 | ||
| Haematopoietic_stem_cells Macrophage Monocyte | ||
| 191 4673 8461 | ||
| Myelocyte Neutrophil NK_cell | ||
| 339 1183 3905 | ||
| Pro-Myelocyte T_cell | ||
| 12 34579 | ||
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| #다쓰면 데이터 너무커서 CIBERSORT에 안올라감 1/8 = 7593 CELL 정도 써야함 (500개이상이면 많은거) | ||
| #500개 random sampling | ||
| #bulkRNA에서 쓴 gene만 보자 | ||
| bulk = read.table(file="/storage2/Project/CSC/RNA/03_Deconvolution/CSC_RNA_TPM_rm0_HUGO_rmdup.txt", header=TRUE, stringsAsFactors=FALSE) | ||
| gene <- bulk$external_gene_name | ||
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| #CIBERSORT_reference_sample_file | ||
| HP_raw_non0 <- as.matrix(HP_raw[Matrix::rowSums(HP_raw)>0,]) | ||
| #write.table(HP_raw_non0,'/storage2/Project/CSC/10X/DGIST_data02/ref_sample.txt',sep = "\t", row.names=TRUE, col.names=TRUE) | ||
| > dim(HP_raw[Matrix::rowSums(HP_raw)>0,]) | ||
| [1] 35126 60749 | ||
| > dim(HP_raw) | ||
| [1] 36826 60749 | ||
| HP_raw_non0_reduced <- HP_raw_non0[which(rownames(HP_raw_non0)%in%gene),which(colnames(HP_raw_non0) %in% cell)] | ||
| > dim(HP_raw_non0_reduced) | ||
| [1] 26008 4109 | ||
| write.table(HP_raw_non0_reduced,'/storage2/Project/CSC/10X/DGIST_data02/ref_sample_reduced.txt',sep = "\t", row.names=TRUE, col.names=TRUE) | ||
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| #CIBERSORT_phenotye_file | ||
| HP_ph <- matrix(,nrow=14,ncol=60749) | ||
| rownames(HP_ph) <- c("T_cell","Monocyte","Macrophage","B_cell","Dendritic","NK_cell","GMP","CMP","Neutrophil","Haematopoietic_stem_cells","Bone_marrow_cells","Erythroblast","Myelocyte","Pro-Myelocyte") | ||
| colnames(HP_ph) <- colnames(HP_raw_non0) | ||
| for(i in 1:ncol(HP_ph)){ | ||
| cell <- colnames(HP_ph)[i] | ||
| celltype <- HP_meta[cell,"cell_label_details"] | ||
| HP_ph[celltype,cell] = 1 | ||
| } | ||
| HP_ph[is.na(HP_ph)] <- 2 | ||
| #write.table(HP_ph,'/storage2/Project/CSC/10X/DGIST_data02/pheno_sample.txt',sep = "\t", row.names=TRUE, col.names=FALSE) | ||
| #reduce tcell | ||
| tcell_list<-sample(colnames(HP_ph[,HP_ph["T_cell",]==1]),500) | ||
| B_list<-sample(colnames(HP_ph[,HP_ph["B_cell",]==1]),500) | ||
| DC_list<-sample(colnames(HP_ph[,HP_ph["Dendritic",]==1]),500) | ||
| Macro_list<-sample(colnames(HP_ph[,HP_ph["Macrophage",]==1]),500) | ||
| Mono_list<-sample(colnames(HP_ph[,HP_ph["Monocyte",]==1]),500) | ||
| Neutro_list<-sample(colnames(HP_ph[,HP_ph["Neutrophil",]==1]),500) | ||
| NK_list<-sample(colnames(HP_ph[,HP_ph["NK_cell",]==1]),500) | ||
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| BM_list<-colnames(HP_ph[,HP_ph["Bone_marrow_cells",]==1]) | ||
| CMP_list<-colnames(HP_ph[,HP_ph["CMP",]==1]) | ||
| Erythro_list<-colnames(HP_ph[,HP_ph["Erythroblast",]==1]) | ||
| GMP_list<-colnames(HP_ph[,HP_ph["GMP",]==1]) | ||
| Haema_list<-colnames(HP_ph[,HP_ph["Haematopoietic_stem_cells",]==1]) | ||
| Myelo_list<-colnames(HP_ph[,HP_ph["Myelocyte",]==1]) | ||
| ProM_list<-colnames(HP_ph[,HP_ph["Pro-Myelocyte",]==1]) | ||
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| cell <- union(union(union(union(union(union(union(union(union(union(union(union(union(tcell_list,B_list),DC_list),Macro_list),Mono_list),Neutro_list),NK_list),BM_list),CMP_list),Erythro_list),GMP_list),Haema_list),Myelo_list),ProM_list) | ||
| #4109 cells | ||
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| HP_ph_reduced<-HP_ph[,which(colnames(HP_ph) %in% cell)] | ||
| write.table(HP_ph_reduced,'/storage2/Project/CSC/10X/DGIST_data02/pheno_sample_reduced.txt',sep = "\t", row.names=TRUE, col.names=FALSE) |