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| library(Seurat)
counts <- Read10X_h5("raw_data/10x_scrna/pbmc10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.h5")
rownames(counts) <- make.unique(rownames(counts))
rna <- CreateSeuratObject(counts = counts, assay = 'RNA', min.cells = 5, min.features = 500, project = '10x_RNA')
scrublet <- read.table("raw_data/10x_scrna/pbmc10k_v3/matrix_doublets.tsv", sep = "\t", col.names = c('observed', 'simulated'))
rownames(scrublet) <- colnames(counts)
rna <- AddMetaData(rna, metadata = scrublet)
rna <- RenameCells(rna, add.cell.id = 'rna')
mito.features <- grep(pattern = "^MT-", x = rownames(x = rna), value = TRUE)
percent.mito <- Matrix::colSums(x = GetAssayData(object = rna, slot = 'counts')[mito.features, ]) / Matrix::colSums(x = GetAssayData(object = rna, slot = 'counts'))
rna$percent.mito <- percent.mito
rna <- subset(x = rna, subset = nCount_RNA > 2000 & nCount_RNA < 20000 & percent.mito < 0.2 & observed < 0.1)
rna <- NormalizeData(rna)
rna <- FindVariableFeatures(rna, nfeatures = 3000)
rna <- ScaleData(rna)
rna <- RunPCA(rna, npcs = 100)
rna <- RunTSNE(rna, dims = 1:30)
rna <- FindNeighbors(rna, dims = 1:30)
rna <- FindClusters(rna, resolution = 0.4, algorithm = 3)
rna <- RunUMAP(rna, graph = 'RNA_nn', metric = 'euclidean')
new.cluster.ids <- c(
"CD14+ Monocytes",
'CD4 Memory',
'CD4 Naive',
'pre-B cell',
'Double negative T cell',
'NK cell',
'B cell progenitor',
'CD8 effector',
'CD8 Naive',
'CD16+ Monocytes',
'Dendritic cell',
'pDC',
'Platelet'
)
names(x = new.cluster.ids) <- levels(x = rna)
rna <- RenameIdents(object = rna, new.cluster.ids)
rna$celltype <- Idents(rna)
nk.cells <- subset(rna, subset = celltype == 'NK cell')
gzmk <- GetAssayData(nk.cells, assay = 'RNA', slot = 'data')['GZMK', ]
nk.cells$bright <- ifelse(gzmk > 1, 'NK bright', 'NK dim')
ctypes <- as.vector(rna$celltype)
names(ctypes) <- names(rna$celltype)
ctypes[Cells(nk.cells)] <- nk.cells$bright
rna <- AddMetaData(rna, metadata = ctypes, col.name = 'celltype')
saveRDS(rna, "seurat_objects/pbmc_10k_v3.rds")
|
