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（reference：Sex-dependent APOE4 neutrophil–microglia interactions drive cognitive impairment in Alzheimer’s disease）
话不多说，看代码（直播视频：哔哩哔哩https://space.bilibili.com/471040659?spm_id_from=333.1007.0.0）：
setwd('D:\\KS项目\\公众号文章\\复现火山图')#Loading packageslibrary(Seurat)library(ggplot2)library(ggrepel)library(cowplot)library(grid)#load data & prepare data for plotuterus <- readRDS("D:/KS项目/公众号文章/RNA速率分析/uterus.rds")SMC <- subset(uterus, celltype=='Smooth muscle cells')EH <- FindMarkers(uterus, ident.1 = "EEC", ident.2 = "HC",logfc.threshold = 0, min.pct = 0.2, group.by = 'orig.ident')#差异基因分析用来构建演示作图数据AH <- FindMarkers(uterus, ident.1 = "AEH", ident.2 = "HC",logfc.threshold = 0, min.pct = 0.2, group.by = 'orig.ident')#差异基因分析用来构建演示作图数据EH$gene <- rownames(EH)#添加gene列AH$gene <- rownames(AH)#添加gene列EH$log_pval <- -log10(EH$p_val_adj)#p值取对数AH$log_pval <- -log10(AH$p_val_adj)#p值取对数AH$log_pval <- -AH$log_pval #这一组的log_pval变成负值，主要是为了图的倒转结合#sig gene for label，or you can chose any genes if you wantsig_EH <- EH[EH$p_val_adj <= 0.01 & abs(EH$avg_log2FC)>=1.2,]sig_AH <- AH[AH$p_val_adj <= 0.01 & abs(AH$avg_log2FC)>=1.2,]#merge datadf <- rbind(EH,AH)#设置自己需要的渐变背景# rect1 <- rasterGrob(alpha(c('#FFF7F3', '#FDE0DD', '#FCC5C0', '#FA9FB5', '#F768A1','#DD3497', '#7A0177'),0.5),# width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)# rect2<- rasterGrob(alpha(c('#fff7fb','#ece7f2','#d0d1e6','#a6bddb','#74a9cf','#3690c0','#0570b0','#045a8d'),0.5),# width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)# rect3<- rasterGrob(c("#FFFFD9", "#EDF8B1", "#C7E9B4", "#7FCDBB", "#41B6C4"), width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)## rect4<- rasterGrob(c("#DC6F58","#F7B698","#FAE7DC","#E1EDF3","#A7CFE4"),width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)rect1 <- rasterGrob(matrix(rev(alpha(c('#FFF7F3', '#FDE0DD', '#FCC5C0', '#FA9FB5', '#F768A1','#DD3497', '#7A0177'),0.5)),nrow = 1),width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)rect2<- rasterGrob(matrix(alpha(c('#fff7fb','#ece7f2','#d0d1e6','#a6bddb','#74a9cf','#3690c0','#0570b0','#045a8d'),0.5),nrow = 1),width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)rect3<- rasterGrob(matrix(rev(c("#FFFFD9", "#EDF8B1", "#C7E9B4", "#7FCDBB", "#41B6C4")),nrow = 1), width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)rect4<- rasterGrob(matrix(rev(c("#DC6F58","#F7B698","#FAE7DC","#E1EDF3")),nrow = 1),width=unit(1,"npc"), height = unit(1,"npc"), interpolate = TRUE)#plotp = ggplot(df, aes(avg_log2FC,log_pval))+annotation_custom(rect1,xmin=-Inf,xmax=-0.3,ymin=20,ymax=Inf)+annotation_custom(rect2, xmin=0.3,xmax=Inf,ymin=20,ymax=Inf)+annotation_custom(rect3,xmin=-Inf,xmax=-0.3,ymin=-20,ymax=-Inf)+annotation_custom(rect4, xmin=0.3,xmax=Inf,ymin=-20,ymax=-Inf)+geom_hline(aes(yintercept=20), color = "#999999", linetype="dashed", size=1) +#添加横线geom_hline(aes(yintercept=-20), color = "#999999", linetype="dashed", size=1) +#添加横线geom_vline(aes(xintercept=-0.3), color = "#999999", linetype="dashed", size=1) + #添加纵线geom_vline(aes(xintercept=0.3), color = "#999999", linetype="dashed", size=1) + #添加纵线geom_point_rast(stroke = 0.5, size=2, shape=21, fill="grey")+geom_point_rast(data = df[abs(df$log_pval) >= 20 & abs(df$avg_log2FC) >=0.3,],aes(fill=avg_log2FC),stroke = 0.5, size=3, shape=21)+##显著基因特别标注，size比非显著的大一点，颜色用logFC表示scale_fill_gradient2(limits=c(-max(df$avg_log2FC), max(df$avg_log2FC)), low="blue", mid="whitesmoke", high = "red", na.value = 'blue')+ #密度颜色设置，并限定范围geom_text_repel(data = sig_EH[sig_EH$avg_log2FC >0,], aes(label=gene),nudge_y = 80,nudge_x = 2,color = 'black', size = 4,fontface = 'italic',min.segment.length = 0,max.overlaps = Inf,box.padding = unit(0.5, 'mm'),point.padding = unit(0.5, 'mm')) + #label需要标注的基因geom_text_repel(data = sig_EH[sig_EH$avg_log2FC <0,], aes(label=gene),nudge_y = 80,nudge_x = -2,color = 'black', size = 4,fontface = 'italic',min.segment.length = 0,max.overlaps = Inf,box.padding = unit(0.5, 'mm'),point.padding = unit(0.5, 'mm')) + #label需要标注的基因geom_text_repel(data = sig_AH[sig_AH$avg_log2FC >0,],aes(label=gene),color = 'black',size = 4, fontface = 'italic',nudge_y = -80,nudge_x = 2,min.segment.length =0,max.overlaps = Inf,point.padding = unit(0.5, 'mm'))+#label需要标注的基因geom_text_repel(data = sig_AH[sig_AH$avg_log2FC <0,],aes(label=gene),color = 'black',size = 4, fontface = 'italic',nudge_y = -80,nudge_x = -2,min.segment.length =0,max.overlaps = Inf,point.padding = unit(0.5, 'mm'))+#label需要标注的基因theme_classic()+ #设置主题theme(aspect.ratio = 1,axis.text.x=element_text(colour="black",size =12),axis.text.y=element_text(colour="black",size =12),axis.ticks=element_line(colour="black"),axis.title = element_blank(),plot.margin=unit(c(0,0,1,0),"line"),legend.direction = 'horizontal',legend.position = 'top',legend.justification=c(1,2),legend.key.width=unit(0.5,"cm"),legend.key.height = unit(0.3, "cm"),legend.title.position = 'top')+ #修改主题标签文字等等geom_rect(aes(xmin =-Inf, xmax = Inf, ymin = 20, ymax = 400),fill = "transparent", color = "red", size = 1.5,linetype="dashed")+geom_rect(aes(xmin =-Inf, xmax = Inf, ymin = -20, ymax = -400),fill = "transparent", color = "blue", size = 1.5,linetype="dashed")+scale_y_continuous(expand = c(0,0),breaks = c(-200,0,200), labels = c(200,0,200))#修改y轴标签p#add other labelggdraw(xlim = c(0, 1), ylim = c(0,1.1))+ # 设置绘图区域的界限draw_plot(p,x = 0, y =0) + # 添加主图（热图）draw_line(x = c(0.3,0.8), y = c(0.01,0.01),lineend = "round",size =1, col = "black", # 添加箭头arrow=arrow(angle = 15, length = unit(0.1,"inches"),type = "closed"))+draw_text(text = "Log2FC", size = 12,x = 0.2, y = 0.02,color="black",fontface = "italic")+draw_line(x = c(0.1,0.1), y = c(0.2,0.85),lineend = "round",size =1, col = "black", # 添加箭头arrow=arrow(angle = 15, length = unit(0.1,"inches"),type = "closed"))+draw_text(text = "-Log(P)", size = 12,angle=90,x = 0.1, y = 0.15,color="black",fontface = "italic")+draw_text(text = "EEC vs HC", size = 12,angle=90,x = 0.9, y = 0.7,color="black",fontface = "bold")+draw_text(text = "AEC vs HC", size = 12,angle=90,x = 0.9, y = 0.3,color="black",fontface = "bold")
效果？杠杠的！
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