https://mp.weixin.qq.com/s/t8XNmcs0xhw9r9SsJ6yyYw
# 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)
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' ,
'FGF7','MME', 'ACTA2',
'PECAM1', 'VWF',
'EPCAM' , 'KRT19', 'PROM1', 'ALDH1A1' )
p_all_markers <- DotPlot(sce.all, features = genes_to_check,
assay='RNA' ) + coord_flip()
# mast cells, TPSAB1 and TPSB2
# B cell, CD79A and MS4A1 (CD20)
# naive B cells, such as MS4A1 (CD20), CD19, CD22, TCL1A, and CD83,
# plasma B cells, such as CD38, TNFRSF17 (BCMA), and IGHG1/IGHG4
genes_to_check = c('CD3D','MS4A1','CD79A',
'CD19', 'CD22', 'TCL1A', 'CD83', # naive B cells
'CD38','TNFRSF17','IGHG1','IGHG4', # plasma B cells,
'TPSAB1' , 'TPSB2', # mast cells,
'PTPRC' )
p <- DotPlot(sce.all, features = genes_to_check,
assay='RNA' ) + coord_flip()
# epi or tumor (EPCAM, KRT19, PROM1, ALDH1A1, CD24).
# - alveolar type I cell (AT1; AGER+)
# - alveolar type II cell (AT2; SFTPA1)
# - secretory club cell (Club; SCGB1A1+)
# - basal airway epithelial cells (Basal; KRT17+)
# - ciliated airway epithelial cells (Ciliated; TPPP3+)
genes_to_check = c( 'EPCAM' , 'KRT19', 'PROM1', 'ALDH1A1' ,
'AGER','SFTPA1','SCGB1A1','KRT17','TPPP3',
'KRT4','KRT14','KRT8','KRT18',
'CD3D','PTPRC' )
p <- DotPlot(sce.all, features = unique(genes_to_check),
assay='RNA' ) + coord_flip()
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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
'FCGR3A',
'LAMP3', 'IDO1','IDO2',## DC3
'CD1E','CD1C', # DC2
'KLRB1','NCR1', # NK
'FGF7','MME', 'ACTA2', ## fibo
'DCN', 'LUM', 'GSN' , ## mouse PDAC fibo
'MKI67' , 'TOP2A',
'PECAM1', 'VWF', ## endo
'EPCAM' , 'KRT19', 'PROM1', 'ALDH1A1' )
#可视化细胞的上述比例情况
feats <- c("nFeature_RNA", "nCount_RNA", "percent_mito", "percent_ribo", "percent_hb")
feats <- c("nFeature_RNA", "nCount_RNA")
p1=VlnPlot(sce.all, features = feats, pt.size = 0 , ncol = 2) +
NoLegend()
p1
library(ggplot2)
ggsave(filename="Vlnplot1.pdf",plot=p1)
feats <- c("percent_mito", "percent_ribo", "percent_hb")
p2=VlnPlot(sce.all, features = feats, pt.size = 0 , ncol = 3, same.y.lims=T) +
scale_y_continuous(breaks=seq(0, 100, 5)) +
NoLegend()
p2
ggsave(filename="Vlnplot2.pdf",plot=p2)
p3=FeatureScatter(sce.all, "nCount_RNA", "nFeature_RNA",
pt.size = 0.5)
ggsave(filename="Scatterplot.pdf",plot=p3)