下图是一篇文献中添加了基因标签的渐变色火山图，今天教大家如何使用R语言绘制一张类似的火山图。来自文章：Cell Rep. 2024;43(3):113784.

1.安装R包

#设置镜像，
 local({r <- getOption("repos")
 r["CRAN"] <- "http://mirrors.tuna.tsinghua.edu.cn/CRAN/"
options(repos=r)})


# 依赖包列表：自动加载并安装
package_list <- c("ggplot2","ggrepel","dplyr","devtools")


# 判断R包加载是否成功来决定是否安装后再加载
for(p in package_list){
  if(!suppressWarnings(suppressMessages(require(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)))){
    install.packages(p,  warn.conflicts = FALSE)
    suppressWarnings(suppressMessages(library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)))
  }
}

2.设置FC和FDR的阈值线

fc_cutoff <- 2
fdr_cutoff <- 0.01

3.读入数据

#设置工作路径
setwd("/share/work")
data<-read.table("Volcano.txt", header=TRUE,sep = "\t")

数据格式如下（表头不要改）：

ID  FDR  log2FC  sign
Trans_newGene_1010  0.1719571  -0.327617885  NA
Trans_newGene_1013  9.65812E-10  -0.905601526  NA
Trans_newGene_1014  0.268121627  0.212668419  NA
Trans_newGene_1026  0.361458835  -0.371175921  NA
Trans_newGene_1027  0.514220593  -0.189095293  NA
Trans_newGene_1055  0.400366453  0.287239534  NA
Trans_newGene_1069  0.038485409  0.667683474  NA
Trans_newGene_1071  0.65302006  0.180040874  NA
Trans_newGene_1084  5.27862E-05  1.314071476  NA

第一列：基因ID；第二列：FDR值；第三列：log2FC值；第四列：要显示的基因名称，不显示的都用NA。

4.添加上下调信息列-regulated

data$regulated <- case_when(data$log2FC > log2(fc_cutoff) & data$FDR < fdr_cutoff ~ "up",
                      data$log2FC < -log2(fc_cutoff) & data$FDR < fdr_cutoff ~ "down",
                      abs(data$log2FC) <= log2(fc_cutoff) ~ "normal",
                      data$FDR >= fdr_cutoff ~ "normal")
head(data)

 添加上下调信息后的数据：

#转换为因子
data$regulated <- factor(data$regulated, levels = c("up","down","normal"), ordered = T)

5.ggplot2绘图

mycolor1 <- c("#008EBE","#FF890E")
top.mar=0.2
right.mar=0.2
bottom.mar=0.2
left.mar=0.2


p <-ggplot(data=data,aes(log2FC,-log10(FDR),color=-log10(FDR))) +
  geom_point(size=2,alpha=0.9)+
  scale_colour_gradient2(low = mycolor1[1],
                         mid = mycolor1[2],
                         high = mycolor1[2],
                         midpoint = 50,             #midpoint = 75 指定颜色渐变的中点，这意味着在数值为75时颜色会是mid指定的颜色
                         guide = "none") +


  geom_point(data = subset(data, !is.na(sign)),
             aes(x = log2FC, y = -log10(FDR)),
             shape = 1, size = 2, stroke = 1.2, color = "black") + # 描边的点


  geom_label_repel(aes(label=sign), fontface="bold",
                   color="grey50", box.padding=unit(0.35, "lines"),
                   point.padding=unit(0.5, "lines"), segment.colour = "grey50")+


  geom_hline(yintercept = c(-log10(fdr_cutoff)),
             linewidth = 0.5,
             color = "orange",
             lty = "dashed")+


  theme_classic()+
  theme(text=element_text(family = "sans",colour ="gray30",size = 10),
        axis.line = element_line(linewidth = 0.6,colour = "gray30"),
        axis.ticks = element_line(linewidth = 0.6,colour = "gray30"),
        axis.ticks.length = unit(1.5,units = "mm"),
        plot.margin=unit(x=c(top.mar,right.mar,bottom.mar,left.mar),
                         units="inches"))
p

结果如下图所示：

好了，小编就先给大家介绍到这里。希望对您的科研能有所帮助！祝您工作生活顺心快乐！
参考文献：Kilfeather, P., Khoo, J. H., Wagner, K., Liang, H., Caiazza, M. C., An, Y., Zhang, X., Chen, X., Connor-Robson, N., Shang, Z., & Wade-Martins, R. (2024). Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease. Cell reports, 43(3), 113784. https://doi.org/10.1016/j.celrep.2024.113784