我这里有很多差异分析的结果，获取这些结果的完整路径
degr = dir("output/016_hot_cold_tumor/DEG", "DESeq2-filtered.csv$",full.names = T,recursive = T)degr[1:4]MedBioInfoCloud: degr[1:4][1] "output/016_hot_cold_tumor/DEG/TCGA-ACC/DESeq2-filtered.csv" [2] "output/016_hot_cold_tumor/DEG/TCGA-BLCA/DESeq2-filtered.csv"[3] "output/016_hot_cold_tumor/DEG/TCGA-BRCA/DESeq2-filtered.csv"[4] "output/016_hot_cold_tumor/DEG/TCGA-CESC/DESeq2-filtered.csv"读入其中一个：
data = read.csv(degr[1],header = T, check.names = F,row.names = 1)查看一下数据：
我这里的差异分析结果文件很多，我选择4个文件读入并合并。
subdeg = degr[1:4]alldeg = do.call(rbind,lapply(subdeg, function(x){ data = read.csv(x,header = T, check.names = F,row.names = 1) data = data[data$gene_type == "protein_coding",] data = data[!duplicated(data[,"gene_name"]),] data$cancer = unlist(strsplit(x,"/"))[4] data$cancer = unlist(strsplit(data$cancer,"-"))[2] return(data)}))合并后添加了1列cancer。
处理一下数据，并增加一列cluster
head(alldeg)alldeg2 = alldeg[alldeg$direction != "Ns",]alldeg2 = arrange(alldeg2,cancer)alldeg2$cancer = factor(alldeg2$cancer)alldeg2$cluster = as.numeric(alldeg2$cancer) - 1MedBioInfoCloud: head(alldeg2)[,(ncol(alldeg2)-3):ncol(alldeg2)] FDR direction cancer cluster27 1.164291e-06 Up ACC 033 1.452798e-09 Down ACC 0182 9.039400e-04 Up ACC 0430 4.099443e-03 Down ACC 0447 4.636853e-11 Down ACC 0469 8.875275e-05 Up ACC 0计算每组差异分析中logFC的最大值和最小值
minlogfc = alldeg2 %>% group_by(cancer) %>% dplyr::slice(which.min(logFC))maxlogfc = alldeg2 %>% group_by(cancer) %>% dplyr::slice(which.max(logFC))根据分组个数，定义用来绘图的数据。
dfbar0 <- data.frame(x=c(0:3), y= maxlogfc$logFC )dfbar1<- data.frame(x=c(0:3), y=minlogfc$logFC)dfcol<- data.frame(x=c(0:3), y=0, label= unique(alldeg2$cancer))绘制背景图：
p <- ggplot()+ geom_col(data = dfbar0, mapping = aes(x = x,y = y), fill = "#dcdcdc",alpha = 0.6)+ geom_col(data = dfbar1, mapping = aes(x = x,y = y), fill = "#dcdcdc",alpha = 0.6)添加散点图：
p1 = p + geom_jitter(data = alldeg2, aes(x = cluster, y = logFC, color = direction), size = 0.85, width =0.4)修改点的颜色：
p2 = p1+ scale_color_manual(name=NULL, values = c("blue","red"))添加注释框：
p3 = p2 + geom_tile(data = dfcol, aes(x=x,y=y), height=2, color = "black", fill = mycol, alpha = 0.6, show.legend = F)添加文本和坐标标题：
p4 = p3 + labs(x="Cancer",y="log2FC") + #添加坐标标题 ##给注释框添加文本 geom_text(data=dfcol, aes(x=x,y=y,label=label), size =6, color ="black")修改主题，需要把横坐标的数值去掉，因为它没有任何意义。
p4 + theme_minimal()+ theme( axis.title = element_text(size = 13, color = "black", face = "bold"), axis.line.y = element_line(color = "black", size = 1), axis.line.x = element_blank(), axis.text.x = element_blank(), axis.text.y = element_text(size = 15,face = "bold", colour = "#1A1A1A"), panel.grid = element_blank(), legend.direction = "vertical", legend.text = element_text(size = 15) )完整代码：
ggplot()+ geom_col(data = dfbar0, mapping = aes(x = x,y = y), fill = "#dcdcdc",alpha = 0.6)+ geom_col(data = dfbar1, mapping = aes(x = x,y = y), fill = "#dcdcdc",alpha = 0.6)+ geom_jitter(data = alldeg2, aes(x = cluster, y = logFC, color = direction), size = 1.5, width =0.4)+ scale_color_manual(name=NULL, values = c("blue","red"))+ geom_tile(data = dfcol, aes(x=x,y=y), height=2, color = "black", fill = mycol, alpha = 0.6, show.legend = F)+ labs(x="Cancer",y="log2FC") + #添加坐标标题 ##给注释框添加文本 geom_text(data=dfcol, aes(x=x,y=y,label=label), size =6, color ="black")+ theme_minimal()+ theme( axis.title = element_text(size = 13, color = "black", face = "bold"), axis.line.y = element_line(color = "black", size = 1), axis.line.x = element_blank(), axis.text.x = element_blank(), axis.text.y = element_text(size = 15,face = "bold", colour = "#1A1A1A"), panel.grid = element_blank(), legend.direction = "vertical", legend.text = element_text(size = 15) )统计所有分组差异基因的频率，找出共同基因并标注出来。
gene_stat = as.data.frame(table(alldeg2$gene_name))gene_stat = arrange(gene_stat,desc(Freq))head(gene_stat)MedBioInfoCloud: head(gene_stat) Var1 Freq1 AOAH 42 ARHGAP9 43 C1QA 44 C1QB 45 C1QC 4总共4个分组的差异分析，频率为4的基因就是共同的差异表达基因。我们选择3个来显示：
gs = gene_stat$Var1[gene_stat$Freq ==4][1:3]gstab = alldeg2[alldeg2$gene_name %in% gs,]gstab = arrange(gstab,cancer)library(ggrepel)fig + geom_text_repel( data=gstab, aes(x=cluster,y=logFC,label=gene_name), force = 1.2, arrow = arrow(length = unit(0.008, "npc"), type = "open", ends = "last") )B站视频合集
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