大鼠单细胞标记基因特异性咋样

library(Seurat)
# https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
## Load the PBMC dataset
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")

## Initialize the Seurat object with the raw (non-normalized data).
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", 
                           min.cells = 3, min.features = 200)
## Identification of mithocondrial genes
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")

## Filtering cells following standard QC criteria.
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & 
                 percent.mt < 5)

## Normalizing the data
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", 
                      scale.factor = 10000)

pbmc <- NormalizeData(pbmc)

## Identify the 2000 most highly variable genes
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)

## In addition we scale the data
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)

pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc), 
               verbose = FALSE)
pbmc <- FindNeighbors(pbmc, dims = 1:10, verbose = FALSE)
pbmc <- FindClusters(pbmc, resolution = 0.5, verbose = FALSE)
pbmc <- RunUMAP(pbmc, dims = 1:10, umap.method = "uwot", metric = "cosine")
table(pbmc$seurat_clusters)
# pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25,  logfc.threshold = 0.25, verbose = FALSE)
DimPlot(pbmc, reduction = "umap", group.by = 'seurat_clusters',
        label = TRUE, pt.size = 0.5) 
DotPlot(pbmc, features = c("MS4A1", "GNLY", "CD3E", 
                               "CD14", "FCER1A", "FCGR3A", 
                               "LYZ", "PPBP", "CD8A"),
        group.by = 'seurat_clusters')
## Assigning cell type identity to clusters
new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T",
                     "FCGR3A+ Mono", "NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
 
pbmc$cluster_by_counts=Idents(pbmc)
table(pbmc$cluster_by_counts)

# T Cells (CD3D, CD3E, CD8A), 
# B cells (CD19, CD79A, MS4A1 [CD20]), 
# Plasma cells (IGHG1, MZB1, SDC1, CD79A), 
# Monocytes and macrophages (CD68, CD163, CD14),
# NK Cells (FGFBP2, FCG3RA, CX3CR1),  
# Photoreceptor cells (RCVRN), 
# Fibroblasts (FGF7, MME), 
# Endothelial cells (PECAM1, VWF). 
# epi or tumor (EPCAM, KRT19, PROM1, ALDH1A1, CD24).
#   immune (CD45+,PTPRC), epithelial/cancer (EpCAM+,EPCAM), 
# stromal (CD10+,MME,fibo or CD31+,PECAM1,endo) 

library(ggplot2) 
genes_to_check = c('PTPRC', 'CD3D', 'CD3E', 'CD4','CD8A',
                   'CD19', 'CD79A', 'MS4A1' ,
                   'IGHG1', 'MZB1', 'SDC1',
                   'CD68', 'CD163', 'CD14', 
                   'TPSAB1' , 'TPSB2',  # mast cells,
                   'RCVRN','FPR1' , 'ITGAM' ,
                   'C1QA',  'C1QB',  # mac
                   'S100A9', 'S100A8', 'MMP19',# monocyte
                   'LAMP3', 'IDO1','IDO2',## DC3 
                   'CD1E','CD1C', # DC2
                   'KLRB1','NCR1', # NK 
                   'FGF7','MME', 'ACTA2', ## fibo 
                   'DCN', 'LUM',  'GSN' , ## mouse PDAC fibo 
                   'Amy1' , 'Amy2a2', # Acinar_cells
                   'PECAM1', 'VWF',  ## endo 
                   'EPCAM' , 'KRT19', 'PROM1', 'ALDH1A1' ) 
th=theme(axis.text.x = element_text(angle = 45, 
                                    vjust = 0.5, hjust=0.5)) 

p_all_markers=DotPlot(sce.all, features = genes_to_check,
                      assay='RNA' ,group.by = 'celltype' )  + coord_flip() +th