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單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（一）批量讀取數(shù)據(jù)

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（二）批量創(chuàng)建Seurat對象及質(zhì)控

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（三）降維、聚類和細胞注釋

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（四）細胞比例餅圖

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（五）細胞亞群并可視化

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（六）標記基因及可視化

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復(fù)現(xiàn)（七）MG 和 Mo/MΦ 評分

這篇文獻的第四個結(jié)果就是觀察了marker基因在Hom-MG、 Act-MG 和 Mo/MΦ 細胞中的表達情況，結(jié)果主要體現(xiàn)在Fig. 4中。

一、將cell_type由三類細分為四類

Hom-MG： h.Microglia <- ctrl_Microglia
Act-MG: a.Microglia <- tumor_Microglia
Mo/MΦ: Macrophages
BAM

table(Idents(seu_object))
#   MG Mo/MΦ   BAM 
#31959  5624  1680
#選出"MG","Mo/MΦ"
seu_object <- subset(seu_object, idents = c("MG","Mo/MΦ"))
table(Idents(seu_object))
#   MG Mo/MΦ 
#31959  5624
#將Microglia細分為"h.Microglia"和"a.Microglia"
Idents(seu_object) <- seu_object$seurat_clusters
seu_object$cell_type_4_groups <- plyr::mapvalues(Idents(seu_object), 
                                     from=c(0:4,6:11,13,14,16,18,19), 
                                     to = c("h.Microglia",  "h.Microglia", "a.Microglia",                                           "h.Microglia", "Macrophages", "h.Microglia",  
                                            "h.Microglia", "Macrophages","a.Microglia",                                             "a.Microglia", "Macrophages","a.Microglia", 
                                            "h.Microglia", "Macrophages" ,"Macrophages",                                            "Macrophages"))
# Figure 4a
# left panel
DimPlot(seu_object, group.by = "cell_type_4_groups")

1.png

二、Act-MG 和 Mo/MΦ 中差異上調(diào)基因的表達水平

數(shù)據(jù)處理

Idents(seu_object) <- seu_object$cell_type_4_groups
#markers_ActMG_vs_HomMG
markers_ActMG_vs_HomMG <- FindMarkers(object = seu_object, ident.1 = "a.Microglia", 
                                      ident.2 = "h.Microglia", only.pos = TRUE, min.pct = 0.25, 
                                      logfc.threshold = 0.25)
markers_ActMG_vs_HomMG$gene <- rownames(markers_ActMG_vs_HomMG)
#markers_MoM_vs_ActMG
markers_MoM_vs_ActMG <- FindMarkers(object = seu_object, ident.1 = "Macrophages", 
                                    ident.2 = "a.Microglia", only.pos = TRUE, min.pct = 0.25, 
                                    logfc.threshold = 0.25)
markers_MoM_vs_ActMG$gene <- rownames(markers_MoM_vs_ActMG)

畫圖

Fig. 4b

#畫圖
genes <- unique(c(markers_ActMG_vs_HomMG$gene, markers_MoM_vs_ActMG$gene))
gene_expression_data <- GetAssayData(object = seu_object, slot = "data")
gene_expression_data <- as.data.frame(t(gene_expression_data[genes, ]))

common_genes <- intersect(markers_ActMG_vs_HomMG$gene, markers_MoM_vs_ActMG$gene)
markers_ActMG_only <- markers_ActMG_vs_HomMG$gene[!(markers_ActMG_vs_HomMG %in% common_genes)]
markers_MoM_only <- markers_MoM_vs_ActMG$gene[!(markers_MoM_vs_ActMG$gene %in% common_genes)]

genes <- unique(c(markers_ActMG_vs_HomMG$gene, markers_MoM_vs_ActMG$gene))
cell_types_selected <- c("h.Microglia", "a.Microglia", "Macrophages")
names(cell_types_selected) <- c("h.Microglia", "a.Microglia", "Macrophages")
genes_mean_expr <- sapply(cell_types_selected, function(cell_type) {
  Matrix::rowMeans(seu_object@assays$RNA@data[genes, 
                                              colnames(seu_object)[seu_object$cell_type_4_groups == cell_type]],
                   na.rm = T)
})

colnames(genes_mean_expr)[1:3]<-c("Hom_MG", "Act_MG", "MoMphi")
genes_mean_expr <- as.data.frame(genes_mean_expr)
genes_mean_expr$gene <- rownames(genes_mean_expr)

labels<-c("Ly6c2", "Ccl5", "Ly6i", "Lyz2",   "Lgals3","Ifitm2", 
          "Tgfbi","Tmsb10", "Il1rn","Ass1","Ifitm3","Il1b", 
          "Irf7","Ccr2", "H2-Aa","H2-Ab1", "H2-Eb1","Cd74","Ifit3",        
          "Il18bp", "Mif", "Apoe", "Stat1", "Ccl12",  "H2-D1",  "Ccl4", "Ccl3", "Ly86")

genes_mean_expr$color <- 0
genes_mean_expr[genes_mean_expr$gene %in% markers_ActMG_only, "color"]<-"Act-MG"
genes_mean_expr[genes_mean_expr$gene %in% markers_MoM_only, "color"]<-"MoM"
genes_mean_expr[genes_mean_expr$gene %in% common_genes, "color"]<-"common"
genes_mean_expr$color<-factor(genes_mean_expr$color, levels=c("Act-MG", "MoM", "common"))

genes_mean_expr_labeled <- genes_mean_expr[labels, ]

col_Macro<-"#FABF00"
col_ActMG<-"#2F8EA1"

pdf(file = "fig4_scat.pdf",width = 10, height = 10)
ggplot(genes_mean_expr, aes(x=Act_MG, y=MoMphi))+
  geom_jitter(aes(fill=color),shape=21, color="white", alpha=0.7, size=4)+
  geom_text_repel(data=genes_mean_expr_labeled, aes(label=gene), nudge_y=0.2, size=5,
                  direction="both")+
  geom_abline(intercept=0, slope=1)+
  scale_fill_manual(values=c(col_ActMG, col_Macro, "black"))+ 
  xlim(0,5)+
  ylim(0,5)+
  xlab("Act-MG")+
  ylab("MoMphi")+
  coord_fixed()+
  theme_bw(base_size = 18)+
  theme(panel.grid = element_blank())
dev.off()

2.png

Fig. 4c
Hom-MG 與 Act-MG 和 Act-MG 與 Mo/MΦ 中前 25 個上調(diào)基因的表達情況

markers_MoM_vs_ActMG <- markers_MoM_vs_ActMG[order(-markers_MoM_vs_ActMG$avg_log2FC),]
markers_ActMG_vs_HomMG <- markers_ActMG_vs_HomMG[order(-markers_ActMG_vs_HomMG$avg_log2FC),]
genes_for_heatmap <- unique(c(markers_MoM_vs_ActMG$gene[1:25], markers_ActMG_vs_HomMG$gene[1:25]))
genes_mean_expr_heatmap <- genes_mean_expr[genes_for_heatmap, ]

mat_breaks <- seq(0, abs(max(genes_mean_expr_heatmap[,1:3])), length=51)

library(pheatmap)
pheatmap(
  mat               = genes_mean_expr_heatmap[, 1:3],
  color             = colorRampPalette(rev(c("#810f7c", "#8856a7", "#8c96c6", "#b3cde3", "#edf8fb")))(length(mat_breaks) - 1),
  breaks            = mat_breaks,
  border_color      = NA,
  cluster_cols      = F,
  cluster_rows      = F,
  show_colnames     = TRUE,
  show_rownames     = TRUE,
  treeheight_col    = 0,
  treeheight_row    = 0,
  gaps_row          = 25,
  drop_levels       = TRUE,
  fontsize          = 10,
  angle_col         = 0,
  main              = "Top upregulated genes in Act-MG and MoM"
)