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Commit 918c0314 authored by Stefano Beretta's avatar Stefano Beretta
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Added initial scripts and data.

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0_PreProcessData.R 0 → 100644
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1_Analysis_TNK.R 0 → 100644
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library(SeuratObject)
library(Seurat)
library(harmony)
library(DoubletFinder)
library(grid)
library(cowplot)
library(gridExtra)
library(RColorBrewer)
library(patchwork)
library(scales)
# Working dir
wdir <- "squadrito_livertumor2022_scrnaseq"
# Data dir
data_dir <- paste(wdir, "data", sep = "/")
# Results dir
out_dir <- paste(wdir, "results", sep = "/")
dir.create(path = out_dir, showWarnings = F)
# Counts
gz_in_counts <- gzfile(paste(data_dir, "Full_final_DBR_labeled_counts.csv.gz", sep = "/"), "rt")
full_counts <- read.csv(gz_in_counts, row.names = 1)
close(gz_in_counts)
# Meta Data
gz_in_md <- gzfile(paste(data_dir, "Full_final_DBR_labeled_metadata.csv.gz", sep = "/"), "rt")
full_md <- read.csv(gz_in_md, row.names = 1)
close(gz_in_md)
# UMAP Coordinates
gz_in_umap <- gzfile(paste(data_dir, "Full_final_DBR_labeled_umap.csv.gz", sep = "/"), "rt")
full_umap <- read.csv(gz_in_umap, row.names = 1)
close(gz_in_umap)
Full_obj <- CreateSeuratObject(counts = full_counts, meta.data = full_md)
##########################
## T cells and NK cells ##
##########################
Full_obj <- SetIdent(object = Full_obj, value = "Newlabels")
TNK_obj <- subset(Full_obj, idents = c("T and NK cells"))
out_dir_TNK <- paste(out_dir, "T_NK_cells", sep = "/")
dir.create(path = out_dir_TNK, showWarnings = F)
saveRDS(TNK_obj, file = paste(out_dir_TNK, "T_NK_cells_DBR.rds", sep = "/"))
# Processing of the subset T cells, (Normalization, SCTransform, PCA and UMAP)
## We are not computing diff expression here so scale data is not done, we will do diff expression on the full obj
TNK_obj@meta.data$CellID <- TNK_obj@assays$RNA@counts@Dimnames[[2]] #CellID -> Will be used latter to be sure IDs are matching with full object
DefaultAssay(object = TNK_obj) <- "RNA"
TNK_obj <- NormalizeData(object = TNK_obj)
TNK_obj <- SCTransform(TNK_obj, vars.to.regress = c("nFeature_RNA", "nCount_RNA", "percent.mt"), verbose = TRUE)
DefaultAssay(object = TNK_obj) <- "SCT"
TNK_obj@misc$analysis_params$max_pca <- 50
TNK_obj <- RunPCA(object = TNK_obj, npcs = TNK_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_TNK, "T_and_NK_Elbowplot.png", sep = "/"))
ElbowPlot(TNK_obj, ndims = TNK_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
TNK_obj@misc$analysis_params$num_pc <- 35
TNK_obj <- RunUMAP(TNK_obj, dims = 1:TNK_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
TNK_obj <- FindNeighbors(TNK_obj, reduction = "pca", dims = 1:TNK_obj@misc$analysis_params$num_pc, force.recalc = T)
TNK_obj <- FindClusters(TNK_obj, resolution = 0.8)
png(filename = paste(out_dir_TNK, "T_and_NK_UMAP_res0.8.png", sep = "/"), width = 1200, height = 1000)
DimPlot(object = TNK_obj, reduction = "umap", group.by = "SCT_snn_res.0.8", pt.size = 1, label = T, label.size = 10)
dev.off()
#Annotate new labels rough on T cells
TNK_obj@meta.data$Newlabels_rough <- rep("Undefined",length(TNK_obj@meta.data$orig.ident))
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "0"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "1"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "2"] <- "NK"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "3"] <- "NKT"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "4"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "5"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "6"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "7"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "8"] <- "ILCs"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "9"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "10"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "11"] <- "CD4 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "12"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "13"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "14"] <- "ILCs"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "15"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "16"] <- "Undefined"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "17"] <- "CD8 T cells"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "18"] <- "NKT"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "19"] <- "Undefined"
TNK_obj@meta.data$Newlabels_rough[TNK_obj@meta.data$SCT_snn_res.0.8 == "20"] <- "gd T cells"
png(filename = paste(out_dir_TNK, "T_and_NK_UMAP_res0.8_Labels.png", sep = "/"), width = 1800, height = 1000)
DimPlot(object = TNK_obj, reduction = "umap", group.by = "SCT_snn_res.0.8", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = TNK_obj, reduction = "umap", group.by = "Newlabels_rough", pt.size = 1)
dev.off()
# Save final object
saveRDS(TNK_obj, file = paste(out_dir_TNK, "T_NK_cells_DBR.rds", sep = "/"))
## NKT - detailed annotation NKT cells ##
# extract NKT cells
TNK_obj <- SetIdent(object = TNK_obj, value = "Newlabels_rough")
TNKonly_obj <- subset(TNK_obj, idents = c("NKT"))
# There are very few cells left in the sample, after trying it many times we realised that it works much better.
# keeping the SCTransform from the T cell and NK object.
# Also the NKT belong to the T cells, so we need them to be somehow taken into consideration when spliting NKT subpopulaitons.
DefaultAssay(object = TNKonly_obj) <- "SCT"
TNKonly_obj@misc$analysis_params$max_pca <- 50
TNKonly_obj <- RunPCA(object = TNKonly_obj, npcs = TNKonly_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_TNK, "NKT_Elbowplot.png", sep = "/"))
ElbowPlot(TNKonly_obj, ndims = TNKonly_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
TNKonly_obj@misc$analysis_params$num_pc <- 30
TNKonly_obj <- RunUMAP(TNKonly_obj, dims = 1:TNKonly_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
TNKonly_obj <- FindNeighbors(TNKonly_obj ,reduction = "pca", dims = 1:TNKonly_obj@misc$analysis_params$num_pc, force.recalc = T)
TNKonly_obj <- FindClusters(TNKonly_obj , resolution = 0.6)
png(filename = paste(out_dir_TNK, "NKT_UMAP_res0.6.png", sep = "/"), width = 1200, height = 1000)
DimPlot(object = TNKonly_obj, reduction = "umap", group.by = "SCT_snn_res.0.6", pt.size = 2, label = T, label.size = 10)
dev.off()
# Cluster Annotation
TNKonly_obj@meta.data$Newlabels_detailed <- rep("Undefined",length(TNKonly_obj@meta.data$orig.ident))
TNKonly_obj@meta.data$Newlabels_detailed[TNKonly_obj@meta.data$SCT_snn_res.0.6 == "0"] <- "MAIT" #Trdc negative, Tcrg-C4 --> clearly alpha beta+ + signs of cytotoxic effect (Gzma/b+ and Prf1+)
TNKonly_obj@meta.data$Newlabels_detailed[TNKonly_obj@meta.data$SCT_snn_res.0.6 == "1"] <- "iNKT" #Sell negative CD8a high CD160 high
TNKonly_obj@meta.data$Newlabels_detailed[TNKonly_obj@meta.data$SCT_snn_res.0.6 == "2"] <- "MAIT" #alpha-beta+ and Ccr2+ Cxcr6+ but Cd7 neg
TNKonly_obj@meta.data$Newlabels_detailed[TNKonly_obj@meta.data$SCT_snn_res.0.6 == "3"] <- "gd NKT"
TNKonly_obj@meta.data$Newlabels_detailed[TNKonly_obj@meta.data$SCT_snn_res.0.6 == "4"] <- "Gzma NKT" #Trac neg, Tcrg-C4/Trdc pos
saveRDS(TNKonly_obj, file = paste(out_dir_TNK, "NKT_DBR.rds", sep = "/"))
# Reintegrate new NKT cell labels into T_NK_cell object
# extract labels from NKT cells
labels_NKT <- data.frame(ToSelect = TNKonly_obj@meta.data$CellID,
Classification = TNKonly_obj@meta.data$Newlabels_detailed)
# integrate NK T cell annotation into T_NK_:cell object
TNK_obj@meta.data$Newlabels_detailed <- TNK_obj@meta.data$Newlabels_rough
alllabels <- labels_NKT
TNK_obj@meta.data$Newlabels_detailed <- TNK_obj@meta.data$Newlabels_rough
orderDF <- data.frame(allIDs = TNK_obj@meta.data$CellID,
order=c(1:length(TNK_obj@meta.data$CellID)))
head(alllabels)
head(orderDF)
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
TNK_obj@meta.data$Newlabels_detailed[TNK_obj@meta.data$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Save final object (after new labels integration)
saveRDS(TNK_obj, file = paste(out_dir_TNK, "T_NK_cells_DBR.rds", sep = "/"))
## CD8 - detailed annotation CD8 T cells ##
# generate CD8 T cell subset
TNK_obj <- SetIdent(object = TNK_obj, value = "Newlabels_rough")
CD8_obj <- subset(TNK_obj, idents = c("CD8 T cells"))
# process CD8 T cell subset
DefaultAssay(object = CD8_obj) <- "SCT"
CD8_obj@misc$analysis_params$max_pca <- 50
CD8_obj <- RunPCA(object = CD8_obj, npcs = CD8_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_TNK, "CD8_Elbowplot.png", sep = "/"))
ElbowPlot(CD8_obj, ndims = CD8_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
CD8_obj@misc$analysis_params$num_pc <- 35
CD8_obj <- RunUMAP(CD8_obj, dims = 1:CD8_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
CD8_obj <- FindNeighbors(CD8_obj, reduction = "pca", dims = 1:CD8_obj@misc$analysis_params$num_pc, force.recalc = T)
CD8_obj <- FindClusters(CD8_obj, resolution = 0.3)
png(filename = paste(out_dir_TNK, "CD8_UMAP_res0.3.png", sep = "/"), width = 1200, height = 1000)
DimPlot(object = CD8_obj, reduction = "umap", group.by = "SCT_snn_res.0.3", pt.size = 2, label = T, label.size = 10)
dev.off()
# CD8 T cell annotation
CD8_obj@meta.data$Newlabels_detailed <- rep("Undefined",length(CD8_obj@meta.data$orig.ident))
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "0"] <- "CD8 Tsm-like cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "1"] <- "CD8 Teff1 cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "2"] <- "CD8 Tex cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "3"] <- "CD8 Tsm-like cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "4"] <- "T prol cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "5"] <- "CD8 Tm cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "6"] <- "CD8 Teff2 cells"
CD8_obj@meta.data$Newlabels_detailed[CD8_obj@meta.data$SCT_snn_res.0.3 == "7"] <- "CD8 Teff1 cells"
png(filename = paste(out_dir_TNK, "CD8_UMAP_res0.3_Labels.png", sep = "/"), width = 1800, height = 1000)
DimPlot(object = CD8_obj, reduction = "umap", group.by = "SCT_snn_res.0.3", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = CD8_obj, reduction = "umap", group.by = "Newlabels_detailed", pt.size = 1)
dev.off()
# save CD8 T cell object
saveRDS(CD8_obj, file = paste(out_dir_TNK, "CD8Tcells_DBR.rds", sep = "/"))
# extract labels from CD8 T cells
labels_CD8 <- data.frame(ToSelect = CD8_obj@meta.data$CellID,
Classification = CD8_obj@meta.data$Newlabels_detailed)
# reintegrate CD8 T cell labels into T_NK_cell object
alllabels <- labels_CD8
orderDF <- data.frame(allIDs = TNK_obj@meta.data$CellID, order = c(1:length(TNK_obj@meta.data$CellID)))
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
TNK_obj@meta.data$Newlabels_detailed[TNK_obj@meta.data$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Save final object (after new labels integration)
saveRDS(TNK_obj, file = paste(out_dir_TNK, "T_NK_cells_DBR.rds", sep = "/"))
## CD4 - detailed annotation CD4 T cells ##
# generation of CD4 T cell object
TNK_obj <- SetIdent(object = TNK_obj, value = "Newlabels_rough")
CD4_obj <- subset(TNK_obj, idents = c("CD4 T cells"))
#process CD4 T cell subset
DefaultAssay(object = CD4_obj) <- "SCT"
CD4_obj@misc$analysis_params$max_pca <- 50
CD4_obj <- RunPCA(object = CD4_obj, npcs = CD4_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_TNK, "CD4_Elbowplot.png", sep = "/"))
ElbowPlot(CD4_obj, ndims = CD4_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
CD4_obj@misc$analysis_params$num_pc <- 35
CD4_obj <- RunUMAP(CD4_obj, dims = 1:CD4_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
CD4_obj <- FindNeighbors(CD4_obj, reduction = "pca", dims = 1:CD4_obj@misc$analysis_params$num_pc, force.recalc = T)
CD4_obj <- FindClusters(CD4_obj, resolution = 0.3)
png(filename = paste(out_dir_TNK, "CD4_UMAP_res0.3.png", sep = "/"), width = 1200, height = 1000)
DimPlot(object = CD4_obj, reduction = "umap", group.by = "SCT_snn_res.0.3", pt.size = 2, label = T, label.size = 10)
dev.off()
#CD4 T cell annotation
CD4_obj@meta.data$Newlabels_detailed <- rep("Undefined",length(CD4_obj@meta.data$orig.ident))
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "0"] <- "CD4 Tr1-like cells"
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "1"] <- "CD4 Tsm-like cells"
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "2"] <- "CD4 Treg cells"
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "3"] <- "CD4 Tr1-like cells"
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "4"] <- "CD4 Tex cells"
CD4_obj@meta.data$Newlabels_detailed[CD4_obj@meta.data$SCT_snn_res.0.3 == "5"] <- "CD4 Teff-like cells"
png(filename = paste(out_dir_TNK, "CD4_UMAP_res0.3_Labels.png", sep = "/"), width = 1800, height = 1000)
DimPlot(object = CD4_obj, reduction = "umap", group.by = "SCT_snn_res.0.3", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = CD4_obj, reduction = "umap", group.by = "Newlabels_detailed", pt.size = 1)
dev.off()
# save CD4 T cell object
saveRDS(CD4_obj, file = paste(out_dir_TNK, "CD4Tcells_DBR.rds", sep = "/"))
#extract labels from CD4 T cells
labels_CD4 <- data.frame(ToSelect = CD4_obj@meta.data$CellID,
Classification = CD4_obj@meta.data$Newlabels_detailed)
#reintegrate CD4 T cell labels into T_NK_cell object
alllabels <- labels_CD4
orderDF <- data.frame(allIDs = TNK_obj@meta.data$CellID, order = c(1:length(TNK_obj@meta.data$CellID)))
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
TNK_obj@meta.data$Newlabels_detailed[TNK_obj@meta.data$CellID%in%alllabels$ToSelect] <- alllabels$Classification
png(filename = paste(out_dir_TNK, "T_and_NK_UMAP_res0.8_Labels_Detailed.png", sep = "/"), width = 2700, height = 1000)
DimPlot(object = TNK_obj, reduction = "umap", group.by = "SCT_snn_res.0.8", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = TNK_obj, reduction = "umap", group.by = "Newlabels_rough", pt.size = 1) |
DimPlot(object = TNK_obj, reduction = "umap", group.by = "Newlabels_detailed", pt.size = 1, label = T, label.size = 6)
dev.off()
# Create files to reintegrate T_NK_cell labels into full object
# extract labels from T_NK_cell label object
T_NK_cell_labels_rough <- data.frame(ToSelect = TNK_obj@meta.data$CellID,
Classification = TNK_obj@meta.data$Newlabels_rough)
T_NK_cell_labels_detailed <- data.frame(ToSelect = TNK_obj@meta.data$CellID,
Classification = TNK_obj@meta.data$Newlabels_detailed)
saveRDS(T_NK_cell_labels_rough, file = paste(out_dir_TNK, "T_NK_cell_labels_rough.rds", sep = "/"))
saveRDS(T_NK_cell_labels_detailed, file = paste(out_dir_TNK, "T_NK_cell_labels_detailed.rds", sep = "/"))
##########################
## Export Table results ##
##########################
# Counts
TNK_counts <- TNK_obj@assays$RNA@counts
gz_out_counts <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_counts.csv.gz", sep = "/"), "w")
write.csv(TNK_counts, gz_out_counts)
close(gz_out_counts)
# Meta Data
TNK_md <- TNK_obj@meta.data
gz_out_md <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_metadata.csv.gz", sep = "/"), "w")
write.csv(x = TNK_md, gz_out_md)
close(gz_out_md)
# UMAP Coordinates
TNK_umap <- TNK_obj@reductions$umap@cell.embeddings
gz_out_umap <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_umap.csv.gz", sep = "/"), "w")
write.csv(x = TNK_umap, gz_out_umap)
close(gz_out_umap)
# Save final object (after new labels integration)
saveRDS(TNK_obj, file = paste(out_dir_TNK, "T_NK_cells_DBR.rds", sep = "/"))
2_Analysis_APC.R 0 → 100644
View file @ 918c0314
library(SeuratObject)
library(Seurat)
library(harmony)
library(DoubletFinder)
library(grid)
library(cowplot)
library(gridExtra)
library(RColorBrewer)
library(patchwork)
library(scales)
# Working dir
wdir <- "squadrito_livertumor2022_scrnaseq"
# Data dir
data_dir <- paste(wdir, "data", sep = "/")
# Results dir
out_dir <- paste(wdir, "results", sep = "/")
dir.create(path = out_dir, showWarnings = F)
# Counts
gz_in_counts <- gzfile(paste(data_dir, "Full_final_DBR_labeled_counts.csv.gz", sep = "/"), "rt")
full_counts <- read.csv(gz_in_counts, row.names = 1)
close(gz_in_counts)
# Meta Data
gz_in_md <- gzfile(paste(data_dir, "Full_final_DBR_labeled_metadata.csv.gz", sep = "/"), "rt")
full_md <- read.csv(gz_in_md, row.names = 1)
close(gz_in_md)
# UMAP Coordinates
gz_in_umap <- gzfile(paste(data_dir, "Full_final_DBR_labeled_umap.csv.gz", sep = "/"), "rt")
full_umap <- read.csv(gz_in_umap, row.names = 1)
close(gz_in_umap)
Full_obj <- CreateSeuratObject(counts = full_counts, meta.data = full_md)
#######################################
## Macrophages and DCs (termed APCs) ##
#######################################
Full_obj <- SetIdent(object = Full_obj, value = "Newlabels")
APC_obj <- subset(Full_obj, idents = c("APCs"))
out_dir_APC <- paste(out_dir, "APCs", sep = "/")
dir.create(path = out_dir_APC, showWarnings = F)
saveRDS(APC_obj, file = paste(out_dir_APC, "APCs_DBR.rds", sep = "/"))
# Processing of the subset Macrophages, (Normalization, SCTransform, PCA and UMAP)
# We are not computing diff expression here so scale data is not done, we will do diff expression on the full obj
APC_obj@meta.data$CellID <- APC_obj@assays$RNA@counts@Dimnames[[2]] #CellID -> Will be used latter to be sure IDs are matching with full object
DefaultAssay(object = APC_obj) <- "RNA"
APC_obj <- NormalizeData(object = APC_obj)
APC_obj <- SCTransform(APC_obj, vars.to.regress = c("nFeature_RNA", "nCount_RNA", "percent.mt"), verbose = TRUE)
DefaultAssay(object = APC_obj) <- "SCT"
APC_obj@misc$analysis_params$max_pca <- 50
APC_obj <- RunPCA(object = APC_obj, npcs = APC_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_APC, "APCs_DBR_Elbowplot.png", sep = "/"))
ElbowPlot(APC_obj, ndims = APC_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
APC_obj@misc$analysis_params$num_pc <- 35
APC_obj <- RunUMAP(APC_obj, dims = 1:APC_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
APC_obj <- FindNeighbors(APC_obj, reduction = "pca", dims = 1:APC_obj@misc$analysis_params$num_pc, force.recalc = T)
APC_obj <- FindClusters(APC_obj, resolution = 1.2)
png(filename = paste(out_dir_APC, "APCs_DBR_UMAP_res1.2.png", sep = "/"), width = 1200, height = 1000)
DimPlot(object = APC_obj, reduction = "umap", group.by = "SCT_snn_res.1.2", pt.size = 2, label = T, label.size = 10)
dev.off()
#Annotate new labels rough on macrophages
APC_obj@meta.data$Newlabels_rough <- rep("Undefined",length(APC_obj@meta.data$orig.ident))
APC_obj@meta.data$CellID <- APC_obj@assays$RNA@counts@Dimnames[[2]]
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "0"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "1"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "2"] <- "Mo DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "3"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "4"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "5"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "6"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "7"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "8"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "9"] <- "Mo DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "10"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "11"] <- "Monocyte & Macrophages"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "12"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "13"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "14"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "15"] <- "Undefined"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "16"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "17"] <- "Undefined"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "18"] <- "Undefined"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "19"] <- "DCs"
APC_obj@meta.data$Newlabels_rough[APC_obj@meta.data$SCT_snn_res.1.2 == "20"] <- "Undefined"
png(filename = paste(out_dir_APC, "APCs_DBR_UMAP_res1.2_Labels.png", sep = "/"), width = 1800, height = 1000)
DimPlot(object = APC_obj, reduction = "umap", group.by = "SCT_snn_res.1.2", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = APC_obj, reduction = "umap", group.by = "Newlabels_rough", pt.size = 1)
dev.off()
#Annotate new labels detailed on Macrophages
APC_obj@meta.data$Newlabels_detailed <- rep("Undefined",length(APC_obj@meta.data$orig.ident))
APC_obj@meta.data$CellID <- APC_obj@assays$RNA@counts@Dimnames[[2]]
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "0"] <- "IFNa TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "1"] <- "TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "2"] <- "Mo DCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "3"] <- "IFNa TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "4"] <- "IFNa TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "5"] <- "Monocytes"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "6"] <- "IFNa TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "7"] <- "IFNa TAMs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "8"] <- "CD8 cDC1"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "9"] <- "Mo DCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "10"] <- "pDCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "11"] <- "KCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "12"] <- "CDPs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "13"] <- "pre DCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "14"] <- "Ccr7 DCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "15"] <- "Undefined"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "16"] <- "pre DCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "17"] <- "Undefined"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "18"] <- "Undefined"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "19"] <- "pDCs"
APC_obj@meta.data$Newlabels_detailed[APC_obj@meta.data$SCT_snn_res.1.2 == "20"] <- "Undefined"
png(filename = paste(out_dir_APC, "APCs_DBR_UMAP_res1.2_Labels_Detailed.png", sep = "/"), width = 1800, height = 1000)
DimPlot(object = APC_obj, reduction = "umap", group.by = "SCT_snn_res.1.2", pt.size = 1, label = T, label.size = 10) |
DimPlot(object = APC_obj, reduction = "umap", group.by = "Newlabels_detailed", pt.size = 1, label = T, label.size = 8)
dev.off()
###Create files to reintegrate T_NK_cell labels into full object
#extract labels from T_NK_cell label object
labels_APC_rough <- data.frame(ToSelect = APC_obj@meta.data$CellID,
Classification = APC_obj@meta.data$Newlabels_rough)
labels_APC_detailed <- data.frame(ToSelect = APC_obj@meta.data$CellID,
Classification = APC_obj@meta.data$Newlabels_detailed)
saveRDS(labels_APC_rough, file = paste(out_dir_APC, "labels_APC_rough.rds", sep = "/"))
saveRDS(labels_APC_detailed, file = paste(out_dir_APC, "labels_APC_detailed.rds", sep = "/"))
##########################
## Export Table results ##
##########################
# Counts
APC_counts <- APC_obj@assays$RNA@counts
gz_out_counts <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_counts.csv.gz", sep = "/"), "w")
write.csv(APC_counts, gz_out_counts)
close(gz_out_counts)
# Meta Data
APC_md <- APC_obj@meta.data
gz_out_md <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_metadata.csv.gz", sep = "/"), "w")
write.csv(x = APC_md, gz_out_md)
close(gz_out_md)
# UMAP Coordinates
APC_umap <- APC_obj@reductions$umap@cell.embeddings
gz_out_umap <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_umap.csv.gz", sep = "/"), "w")
write.csv(x = APC_umap, gz_out_umap)
close(gz_out_umap)
# Save final object
saveRDS(APC_obj, file = paste(out_dir_APC, "APCs_DBR.rds", sep = "/"))
3_Analysis_UndRem.R 0 → 100644
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library(ggplot2)
library(Seurat)
library(openxlsx)
########################
### General Settings ###
########################
# Working dir
wdir <- "~/Downloads/TIGET/Squadrito/scSquadrito/squadrito_livertumor2022_scrnaseq"
# Data dir
data_dir <- paste(wdir, "data", sep = "/")
# Results dir
out_dir <- paste(wdir, "results", sep = "/")
dir.create(path = out_dir, showWarnings = F)
# Full dir
full_dir <- paste(out_dir, "Full", sep = "/")
dir.create(path = full_dir, showWarnings = F)
# Plot dir
plot_dir <- paste(full_dir, "plots", sep = "/")
dir.create(path = plot_dir, showWarnings = F)
# Full Counts
gz_in_counts <- gzfile(paste(data_dir, "Full_final_DBR_labeled_counts.csv.gz", sep = "/"), "rt")
full_counts <- read.csv(gz_in_counts, row.names = 1)
close(gz_in_counts)
# Full Meta Data
gz_in_md <- gzfile(paste(data_dir, "Full_final_DBR_labeled_metadata.csv.gz", sep = "/"), "rt")
full_md <- read.csv(gz_in_md, row.names = 1)
close(gz_in_md)
###################################
## Reintegrate APCS and T-NK-NKT ##
## cells labels into full object ##
###################################
# TNK Meta Data
gz_in_md <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_metadata.csv.gz", sep = "/"), "rt")
TNK_md <- read.csv(gz_in_md, row.names = 1)
close(gz_in_md)
T_NK_cell_labels_rough <- data.frame(ToSelect = TNK_md$CellID, Classification = TNK_md$Newlabels_rough)
T_NK_cell_labels_detailed <- data.frame(ToSelect = TNK_md$CellID, Classification = TNK_md$Newlabels_detailed)
# APC Meta Data
gz_in_md <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_metadata.csv.gz", sep = "/"), "rt")
APC_md <- read.csv(gz_in_md, row.names = 1)
close(gz_in_md)
labels_APC_rough <- data.frame(ToSelect = APC_md$CellID, Classification = APC_md$Newlabels_rough)
labels_APC_detailed <- data.frame(ToSelect = APC_md$CellID, Classification = APC_md$Newlabels_detailed)
# Reintegrate T_NK_cell_rough labels into full object
alllabels <- T_NK_cell_labels_rough
full_md$Newlabels_rough <- full_md$Newlabels
orderDF <- data.frame(allIDs = full_md$CellID, order = c(1:length(full_md$CellID)))
head(alllabels)
head(orderDF)
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
full_md$Newlabels_rough[full_md$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Reintegrate T_NK_cell_detailed labels into full object
alllabels <- T_NK_cell_labels_detailed
full_md$Newlabels_detailed <- full_md$Newlabels
orderDF <- data.frame(allIDs = full_md$CellID, order = c(1:length(full_md$CellID)))
head(alllabels)
head(orderDF)
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
full_md$Newlabels_detailed[full_md$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Reintegrate rough APC cell labels into full object
alllabels <- labels_APC_rough
orderDF <- data.frame(allIDs = full_md$CellID, order = c(1:length(full_md$CellID)))
head(alllabels)
head(orderDF)
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
full_md$Newlabels_rough[full_md$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Reintegrate detailed APC cell labels into full object
alllabels <- labels_APC_detailed
orderDF <- data.frame(allIDs = full_md$CellID, order = c(1:length(full_md$CellID)))
head(alllabels)
head(orderDF)
alllabels <- merge(alllabels, orderDF, by = 1)
alllabels <- alllabels[order(alllabels$order),]
full_md$Newlabels_detailed[full_md$CellID%in%alllabels$ToSelect] <- alllabels$Classification
# Save Full Meta data
gz_out_md <- gzfile(paste(data_dir, "Full_final_DBR_labeled_metadata.csv.gz", sep = "/"), "w")
write.csv(x = full_md, gz_out_md)
close(gz_out_md)
############################
## Remove Undefined Cells ##
############################
Full_obj <- CreateSeuratObject(counts = full_counts, meta.data = full_md)
Full_obj <- SetIdent(object = Full_obj, value = "Newlabels_detailed")
Full_obj <- subset(Full_obj, idents = "Undefined", invert = T)
# Reperform clustering after removal of undefined cells
Full_obj@meta.data$CellID <- Full_obj@assays$RNA@counts@Dimnames[[2]] #CellID -> Will be used latter to be sure IDs are matching with full object
DefaultAssay(object = Full_obj) <- "RNA"
Full_obj <- NormalizeData(object = Full_obj)
Full_obj <- SCTransform(Full_obj, vars.to.regress = c("CC.Difference", "percent.mt", "nCount_RNA"), verbose = TRUE)
DefaultAssay(object = Full_obj) <- "SCT"
Full_obj@misc$analysis_params$max_pca <- 50
Full_obj <- RunPCA(object = Full_obj, npcs = Full_obj@misc$analysis_params$max_pca)
png(filename = paste(plot_dir, "Full_und_rem_Elbowplot.png", sep = "/"))
ElbowPlot(Full_obj, ndims = Full_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
Full_obj@misc$analysis_params$num_pc <- 35
Full_obj <- RunUMAP(Full_obj, dims = 1:Full_obj@misc$analysis_params$num_pc, seed.use = 20, reduction = "pca")
Full_obj <- FindNeighbors(Full_obj, reduction = "pca", dims = 1:Full_obj@misc$analysis_params$num_pc, force.recalc = T)
### Table Markers Fullobj ###
DefaultAssay(object = Full_obj) <- "RNA"
Full_obj <- SetIdent(Full_obj, value = "Newlabels")
tableFullobj <- FindAllMarkers(object = Full_obj, logfc.threshold = 0.25, min.pct = 0.1, test.use = "wilcox")
write.xlsx(x = list("Allcells_Newlabels_AllMarkersF" = tableFullobj), file = paste(data_dir, "scRNAseq_DiFgenes_Fullobj_FC0.25.xlsx", sep = "/"))
# Save full object
saveRDS(object = Full_obj, paste(full_dir, "Full_final_DBR_labeled_Und_rem.rds", sep = "/"))
# Recluster T and NK cells
Full_obj <- SetIdent(object = Full_obj, value = "Newlabels")
TNK_obj <- subset(Full_obj, idents = "T and NK cells")
out_dir_TNK <- paste(out_dir, "T_NK_cells", sep = "/")
dir.create(out_dir_TNK, showWarnings = F)
TNK_obj@meta.data$CellID <- TNK_obj@assays$RNA@counts@Dimnames[[2]] #CellID -> Will be used latter to be sure IDs are matching with full object
DefaultAssay(object = TNK_obj) <- "RNA"
TNK_obj <- NormalizeData(object = TNK_obj)
TNK_obj <- SCTransform(TNK_obj, vars.to.regress = c("CC.Difference", "percent.mt", "nCount_RNA"), verbose = TRUE)
DefaultAssay(object = TNK_obj) <- "SCT"
TNK_obj@misc$analysis_params$max_pca <- 50
TNK_obj <- RunPCA(object = TNK_obj, npcs = TNK_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_TNK, "T_and_NK_Und_rem_Elbowplot.png", sep = "/"))
ElbowPlot(TNK_obj, ndims = TNK_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
TNK_obj@misc$analysis_params$num_pc <- 35
TNK_obj <- RunUMAP(TNK_obj, dims = 1:TNK_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
TNK_obj <- FindNeighbors(TNK_obj, reduction = "pca", dims = 1:TNK_obj@misc$analysis_params$num_pc, force.recalc = T)
### Table Markers T&NKcells ###
DefaultAssay(object = TNK_obj) <- "RNA"
TNK_obj <- SetIdent(TNK_obj, value = "Newlabels_detailed")
tableTNKcells <- FindAllMarkers(TNK_obj, logfc.threshold = 0.25, min.pct = 0.1, test.use = "wilcox")
write.xlsx(x = list("T_NK_cells_Newlabels_detailed_A" = tableTNKcells), file = paste(data_dir, "scRNAseq_DIFgenes_T&NKcells_FC0.25.xlsx", sep = "/"))
# Save T_NK object
saveRDS(object = TNK_obj, paste(out_dir_TNK, "T_NK_final_DBR_labeled_Und_rem.rds", sep = "/"))
# Recluster APCs
Full_obj <- SetIdent(object = Full_obj, value = "Newlabels")
APC_obj <- subset(Full_obj, idents = "APCs")
out_dir_APC <- paste(out_dir,"APCs", sep = "/")
dir.create(out_dir_APC, showWarnings = F)
APC_obj@meta.data$CellID <- APC_obj@assays$RNA@counts@Dimnames[[2]] #CellID -> Will be used latter to be sure IDs are matching with full object
DefaultAssay(object = APC_obj) <- "RNA"
APC_obj <- NormalizeData(object = APC_obj)
APC_obj <- SCTransform(APC_obj, vars.to.regress = c("CC.Difference", "percent.mt", "nCount_RNA"), verbose = TRUE)
DefaultAssay(object = APC_obj) <- "SCT"
APC_obj@misc$analysis_params$max_pca <- 50
APC_obj <- RunPCA(object = APC_obj, npcs = APC_obj@misc$analysis_params$max_pca)
png(filename = paste(out_dir_APC, "APCs_DBR_Elbowplot.png", sep = "/"))
ElbowPlot(APC_obj, ndims = APC_obj@misc$analysis_params$max_pca, reduction = "pca")
dev.off()
APC_obj@misc$analysis_params$num_pc <- 35
APC_obj <- RunUMAP(APC_obj, dims = 1:APC_obj@misc$analysis_params$num_pc, seed.use = 1, reduction = "pca")
APC_obj <- FindNeighbors(APC_obj, reduction = "pca", dims = 1:APC_obj@misc$analysis_params$num_pc, force.recalc = T)
### Table Markers APCs ###
DefaultAssay(object = APC_obj) <- "RNA"
APC_obj <- SetIdent(APC_obj, value = "Newlabels_detailed")
tableAPCs <- FindAllMarkers(object = APC_obj, logfc.threshold = 0.25, min.pct = 0.1, test.use = "wilcox")
write.xlsx(x = list("APCs_Newlabels_detailed_AllMark" = tableAPCs), file = paste(data_dir, "scRNAseq_DIFgenes_APCs_FC0.25.xlsx", sep = "/"))
# Save APC
saveRDS(object = APC_obj, paste(out_dir_APC, "APC_final_DBR_labeled_Und_rem.rds", sep = "/"))
##########################
## Export Table results ##
##########################
data_dir <- paste(wdir, "data", sep = "/")
dir.create(path = data_dir, showWarnings = F)
## Full ##
# Counts
full_counts <- Full_obj@assays$RNA@counts
gz_out_counts <- gzfile(paste(data_dir, "Full_final_DBR_labeled_Und_rem_counts.csv.gz", sep = "/"), "w")
write.csv(full_counts, gz_out_counts)
close(gz_out_counts)
# Meta Data
full_md <- Full_obj@meta.data
gz_out_md <- gzfile(paste(data_dir, "Full_final_DBR_labeled_Und_rem_metadata.csv.gz", sep = "/"), "w")
write.csv(x = full_md, gz_out_md)
close(gz_out_md)
# UMAP Coordinates
full_umap <- Full_obj@reductions$umap@cell.embeddings
gz_out_umap <- gzfile(paste(data_dir, "Full_final_DBR_labeled_Und_rem_umap.csv.gz", sep = "/"), "w")
write.csv(x = full_umap, gz_out_umap)
close(gz_out_umap)
## TNK ##
# Counts
TNK_counts <- TNK_obj@assays$RNA@counts
gz_out_counts <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_Und_rem_counts.csv.gz", sep = "/"), "w")
write.csv(TNK_counts, gz_out_counts)
close(gz_out_counts)
# Meta Data
TNK_md <- TNK_obj@meta.data
gz_out_md <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_Und_rem_metadata.csv.gz", sep = "/"), "w")
write.csv(x = TNK_md, gz_out_md)
close(gz_out_md)
# UMAP Coordinates
TNK_umap <- TNK_obj@reductions$umap@cell.embeddings
gz_out_umap <- gzfile(paste(data_dir, "T_NK_cells_DBR_labeled_Und_rem_umap.csv.gz", sep = "/"), "w")
write.csv(x = TNK_umap, gz_out_umap)
close(gz_out_umap)
## APCs ##
# Counts
APC_counts <- APC_obj@assays$RNA@counts
gz_out_counts <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_Und_rem_counts.csv.gz", sep = "/"), "w")
write.csv(APC_counts, gz_out_counts)
close(gz_out_counts)
# Meta Data
APC_md <- APC_obj@meta.data
gz_out_md <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_Und_rem_metadata.csv.gz", sep = "/"), "w")
write.csv(x = APC_md, gz_out_md)
close(gz_out_md)
# UMAP Coordinates
APC_umap <- APC_obj@reductions$umap@cell.embeddings
gz_out_umap <- gzfile(paste(data_dir, "APCs_cells_DBR_labeled_Und_rem_umap.csv.gz", sep = "/"), "w")
write.csv(x = APC_umap, gz_out_umap)
close(gz_out_umap)
data/.DS_Store 0 → 100644
View file @ 918c0314
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