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Science杂志：比火山图多一点信息的火山图

前面我们在学习这篇 2024 年 6 月份发表在 顶刊 science 杂志上的文献《Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers》时，发现里面的图片都很美观，我们可以借来放在自己的科研文章中以提升档次。

今天再来学习文章中的一幅不同寻常的火山图，相比于常规的火山图，这个图在底部展示了两个Hallmark KRAS signaling gene sets基因集的富集结果，竖线表示基因集中的基因在横轴即所有基因按照 FC 值排序的基因中的位置，就是GSEA结果中的那种排序竖线。图片如下：

图注：Fig. 1. Establishment and evaluation of a KRAS dependent gene expression program in KRAS-mutant PDAC.

图中的元素：

  • 全部的点：(A) KRAS-dependent gene expression changes upon acute (24 hours) KRAS suppression in eight KRAS mutant PDAC cell lines transiently transfected with KRAS or control nonspecific (NS) siRNA.
    • 淡蓝色的实心点：展示三个key基因（KRAS, FOSL1, MYC）
    • 橙色的实心点：KRAS_SIGNALING_UP 基因集中的基因
    • 灰色的实心点：KRAS_SIGNALING_DN 基因集中的基因
  • 底部的两个hallmark基因集，使用竖线表示：The enrichment of Hallmark KRAS signaling gene sets is shown at the bottom.
  • 差异上调和下调基因，使用浅蓝色和浅红色的框框框起来了：The 677 KRAS-dependent (UP) and 1051 KRAS-inhibited (DN) genes (log2FC > 0.5, adj. p < 0.05) are indicated by the light blue– and light red–shaded rectangles underlaid on plot, respectively.
  • 虚线框框，选取的 top200 差异基因：The top 200 KRAS dependent (UP) and KRAS inhibited (DN) genes that make up the PDAC KRAS UP/DN signatures are indicated by the dotted outlines. 选择的标准：FDR<0.01的时候，按照 FC 从大到小排序取top200

火山图背景

PDAC细胞系KRAS siRNA RNA测序

为了确定 KRAS 依赖性转录组，作者对一组八个人类 KRAS 突变型胰腺导管腺癌 PDAC 的细胞系在经过 24 小时 KRAS siRNA 处理后的基因转录变化（图 S1A 和 B）进行了 RNA-seq 测序。fq数据可在这里下载：PRJNA980201 。Note：

  • NS缩写：control non-specific ("NS") siRNA；
  • KRAS siRNA：是一种小干扰RNA（siRNA），通过特异性靶向KRAS基因的mRNA，从而抑制其表达，达到治疗KRAS突变肿瘤的目的。

差异分析结果如下：The 677 KRAS-dependent (UP) and 1051 KRAS-inhibited (DN) genes (|log2FC| > 0.5, adj. p < 0.05) are indicated by the light blue– and light red–shaded rectangles underlaid on plot, respectively。

差异结果在表格S1：Data S1. Differential expression statistics for genes in KRAS versus NS siRNA treated PDAC cells, using RNA-sequencing。

差异结果读取

读取表格：附表表格S1，并预处理数据

代码语言：javascript代码运行次数：0运行复制
###
### Create: juan zhang
### Date:   2025-01-16
### Email:  492482942@qq
### Blog:   /
### Forum:  .html
### Update Log: 2025-01-16   First version 
### 

rm(list=ls())
# 加载R包
library(ggplot2)
library(tibble)
library(ggrepel)
library(tidyverse)
library(dplyr)
library(patchwork)
library(ggplot2)

##### 01、加载数据
# 加载：KRAS vs NS siRNA（log2FC）
diff <- read.csv("./data/science.adk0775_data_s1.csv" )
head(diff)

# 提取基因ID,基因Symbol,FDR值和FC值
diff <- diff[,c("X","external_gene_name","logFC","FDR" )]

# 增加一列上下调，阈值 log2FC > 0.5, adj. p < 0.05
diff$g <- "normal"
diff$g[diff$logFC >0.5 & diff$FDR < 0.05 ] <- "up"
diff$g[diff$logFC < -0.5 & diff$FDR < 0.05 ] <- "down"
table(diff$g)
# down normal     up 
# 677  13071   1051 

# 显著差异基因
diff_up <- diff[diff$g=="up", ]
diff_down <- diff[diff$g=="down", ]

# 增加 top200, 筛选条件：FDR<0.01的时候，按照 FC 从大到小排序取top200
top200 <- diff[ diff$FDR<0.01 , ]
top200_up <- top200[order(top200$logFC, decreasing = T)[1:200], ]
top200_down <- top200[order(top200$logFC, decreasing = F)[1:200], ]

两个 Hallmark基因集

去 GSEA 的 MSigDB 数据库去下载Hallmark基因集 gmt 格式：.jsp?filePath=/msigdb/release/2024.1.Hs/h.all.v2024.1.Hs.symbols.gmt

代码语言：javascript代码运行次数：0运行复制
# 读取 hallmark基因集
library(clusterProfiler)
geneset <- read.gmt("data/h.all.v2024.1.Hs.symbols.gmt")
HALLMARK_KRAS_SIGNALING_DN <- geneset[geneset$term=="HALLMARK_KRAS_SIGNALING_DN", 2]
HALLMARK_KRAS_SIGNALING_UP <- geneset[geneset$term=="HALLMARK_KRAS_SIGNALING_UP", 2]
HALLMARK_KRAS_SIGNALING_UP
HALLMARK_KRAS_SIGNALING_DN

diff$gene_col <- "all"
diff$gene_col[diff$external_gene_name %in% c("KRAS", "FOSL1", "MYC") ] <- "key"
diff$gene_col[diff$external_gene_name %in% HALLMARK_KRAS_SIGNALING_UP ] <- "KRAS_UP"
diff$gene_col[diff$external_gene_name %in% HALLMARK_KRAS_SIGNALING_DN ] <- "KRAS_DN"
table(diff$gene_col)

# 三类点
data_bg <- diff[diff$gene_col=="all", ]
data_geneset <- diff[grepl("KRAS",diff$gene_col), ]
data_KRAS_UP <- diff[grepl("KRAS_UP",diff$gene_col), ]
data_KRAS_DN <- diff[grepl("KRAS_DN",diff$gene_col), ]
data_key <- diff[diff$gene_col=="key", ]

ggplot2绘图

先画基本点

我这里在画图的时候，发现 如果先画点，后面画阴影区域，阴影就会把点的图层覆盖，这是因为 ggplot2 中绘图图层的绘制顺序是从下到上的，需要注意这一点：

代码语言：javascript代码运行次数：0运行复制
## ggplot2 绘图
# 先画添加阴影区，在画点的图层

p <- ggplot(diff, aes(x = logFC, y = -log10(FDR)))  + 
  annotate(geom = "rect",fill = "#ea838e", alpha = 0.3, xmin = min(diff_up$logFC), xmax = max(diff_up$logFC),      # 显著上调阴影  
           ymin = -log10(0.05), ymax = max(-log10(diff$FDR)) ) + 
  annotate(geom = "rect",fill = "#827fd6", alpha = 0.3, xmin = min(diff_down$logFC), xmax = max(diff_down$logFC),  # 显著下调阴影
           ymin = -log10(0.05), ymax = max(-log10(diff$FDR)) ) + 
  geom_point(data = data_bg, shape = 21, color = "black", alpha = 0.2, size = 1.3, stroke = 0.6) +  # 背景灰色点：shape=21带边框的圆形，stroke点的边框宽度
  geom_point(data = rbind(data_geneset,data_key), aes(fill = gene_col), shape = 21, color = "black", size = 2, stroke = 0.8) +  # 通路中的点
  scale_fill_manual(values = c("KRAS_UP" = "#faa400", "KRAS_DN" = "#bebebe","key"="#0afdfe")) +     # 通路和关键基因的点的颜色
  geom_label_repel(data = data_key, aes(label = external_gene_name),size = 6,  fill = "white",      # 三个关键基因
                   box.padding=unit(0.35, "lines"), alpha = 0.8,  # 更高的透明度
                   point.padding = 0.5,     # 设置文本标签与对应点之间的最小距离
                   segment.colour = "#4c4b5e",
                   segment.size=0.5,        # 设置引导线的粗细
                   min.segment.length = 0) +  # 长度大于0就可以添加引线
  geom_vline(xintercept = 0, color = "#b2b2b2", linewidth = 0.6) + # 添加一条竖线
  geom_hline(yintercept = 0, color = "#b2b2b2", linewidth = 0.6) + # 添加 水平线
  geom_hline(yintercept = -log10(0.05), color = "#ff7d82", linewidth = 0.6, linetype = "dotted") + # 添加显著性虚水平线
  scale_x_continuous(limits = c(-3, 3), breaks = seq(-3, 3, 1)) + 
  theme_classic() + 
  theme (legend.position = "none",
         axis.title.x = element_text(size = 16),
         axis.title.y = element_text(size = 16),
         axis.text.x = element_text(size = 12),
         axis.text.y = element_text(size = 12))

p

结果如下：

添加top200的虚线框框 和 文字标签：

代码语言：javascript代码运行次数：0运行复制
# 添加top200的虚线框框 和 文字标签
p1 <- p + 
  annotate("rect", fill = "transparent", linetype = "dotted", color = "black", linewidth = 0.7, xmin = min(top200_up$logFC), xmax = max(top200_up$logFC), 
           ymin = -log10(0.01), ymax = max(-log10(diff$FDR))) +
  annotate("rect",  fill = "transparent", linetype = "dotted", color = "black", linewidth = 0.7,xmin = min(top200_down$logFC), xmax = max(top200_down$logFC), 
           ymin = -log10(0.01), ymax = max(-log10(diff$FDR))) +
  annotate("text", x = -2.1, y = 6.1, label = "PDAC KRAS UP\n(KRAS-dependent)", color = "#827fd6", size = 5, lineheight = 0.9, vjust = 0) + 
  annotate("text", x = 2.1, y = 6.1, label = "PDAC KRAS DOWN\n(KRAS-inhibited)", color = "#ea838e", size = 5, lineheight = 0.9, vjust = 0) + 
  annotate("text", x = -2.5, y = 5.9, label = "Top 200", color = "black", size = 5.5) +
  annotate("text", x = 2.5, y = 5.9, label = "Top 200", color = "black", size = 5.5) +
  annotate("text", x = 2.8, y = 1.1, label = "α = 0.05", color = "#ea838e", size = 4.5) +
  xlab(label = "KRAS vs NS siRNA (log2FC)") + 
  ylab(label =  "Significance (-log10 adj.p-val.)") +
  scale_y_continuous(limits = c(-2, 7), breaks = seq(0, 6.5, 2), expand = c(0, 0))

p1

到这里上半部分就画好了：

添加hallmak通路

下半部分：

代码语言：javascript代码运行次数：0运行复制
# 添加hallmak通路 
p2 <- p1 + 
  geom_linerange(data = data_KRAS_UP, aes(x = logFC, ymin = -1, ymax = -0.35), color = "#faa400", size = 0.5, linewidth = 0.1) +
  geom_linerange(data = data_KRAS_DN, aes(x = logFC, ymin = -1.75, ymax = -1.1), color = "#bebebe", size = 0.5, linewidth = 0.1) +
  annotate("text", x=-3, y = -0.7, label = "KRAS_SIGNALING_UP", color = "#faa400", size = 4.8, hjust = 0) + # 左对齐
  annotate("text", x=-3, y = -1.4, label = "KRAS_SIGNALING_DN", color = "#bebebe", size = 4.8, hjust = 0) +
  annotate("text", x=3.0, y = -0.7, label = "ES: -0.51\npval: 4.9e-12", color = "#faa400", size = 4.5, lineheight = 0.8, hjust = 1) + # 右对齐
  annotate("text", x=3.0, y = -1.4, label = "ES: 0.54\npval: 1.6e-8", color = "#bebebe", size = 4.5, lineheight = 0.8, hjust = 1)
p2

最后是保存

有个希腊字母 α：

代码语言：javascript代码运行次数：0运行复制
# 保存,有个希腊字母 α
library(extrafont)
font_import()
loadfonts()
library(Cairo)
CairoPDF("Fig.1A.pdf", width = 8, height = 6)
print(p2)
dev.off()
本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。原始发表：2025-03-22，如有侵权请联系 cloudcommunity@tencent 删除表格排序数据diffsize

本文标签： Science杂志比火山图多一点信息的火山图

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