ggplot2优雅的绘制配对气泡图

生信菜鸟团

发布于 2023-08-23 09:00:39

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发布于 2023-08-23 09:00:39

文章被收录于专栏：生信菜鸟团

Step1. R包和数据加载、主题设置

测试数据在：

链接：https://pan.baidu.com/s/1MuMgMZZCcdO-IGS7_ysfkQ?pwd=1234 提取码：1234

library(dplyr)
library(ggplot2)
library(ggpubr)
library(rstatix)
#### 1.读入数据
pan.meta = readRDS("./Paired_test.data.rds")

head(pan.meta)
#   ID    Group Type      paired Expression
# 1 TCGA-BL-A13J-01A tumor BLCA TCGA-BL-A13J   5.418168
# 2 TCGA-BT-A20N-01A tumor BLCA TCGA-BT-A20N   2.472168
# 3 TCGA-BT-A20Q-01A tumor BLCA TCGA-BT-A20Q   5.149659
# 4 TCGA-BT-A20R-01A tumor BLCA TCGA-BT-A20R   5.590747
# 5 TCGA-BT-A20U-01A tumor BLCA TCGA-BT-A20U   6.717855
# 6 TCGA-BT-A20W-01A tumor BLCA TCGA-BT-A20W   7.140064

ID是TCGA的barcode，Group是肿瘤与对照样本，paired是配对的barcode信息，Expression则是目标基因的表达量。

然后设置主题和颜色：

mytheme <- theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),units=,"cm"),
                 axis.line = element_line(color = "black",size = 0.4),
                 panel.grid.minor = element_blank(),
                 panel.grid.major = element_line(size = 0.2,color = "#e5e5e5"),
                 axis.text.y = element_text(color="black",size=10),
                 axis.text.x = element_text(angle = 45, hjust = 1 ,color="black",size=10),
                 legend.position = "none",
                 panel.spacing = unit(0,"lines"))
fill.color = c(normal="#56B4E9",tumor= "#E69F00")

Step2. 可视化绘图

p1 = ggplot(pan.meta, aes(x = Group,y = Expression,fill = Group)) +
  geom_line(aes(group=paired),position = position_dodge(0.2),color="grey80") +
  geom_point(aes(group=paired,size=Expression),
             pch=21,
             size=3,
             # position = position_dodge(0),
             position = position_jitter(w = 0.1))+
  stat_summary(fun.data = 'mean_se', geom = "errorbar", color = "red", 
               width = 0.25, size = 0.8, position = position_dodge( .9)) +
  # scale_size_continuous(range=c(1,3)) +
  facet_wrap(.~Type,scales = "free_y",ncol = 11) +
  scale_fill_manual(values = fill.color) +
  # scale_y_continuous(limits = c(-4,4),minor_breaks = seq(0,90,1)) +
  labs(x= NULL,y="Gene expression")+
  stat_compare_means(aes(group =  Group),
                     paired = T,
                     # label.y = 3.5, 
                     label.x = 1.5,
                     method = "t.test",
                     label = "p.signif",
                     size = 4) + 
  theme_bw() + mytheme
p1

image-20230616113300005

上面的P值是用stat_compare_means计算的，其实多组间的两两比较还可以考虑用校正后的P值，可以使用rstatix包进行计算：

stat.test<- pan.meta %>%
  group_by(Type) %>%
  t_test(Expression~Group) %>%
  adjust_pvalue(method = "bonferroni") %>%
  add_significance("p.adj")%>%
  add_xy_position(x="Group",dodge = 0.8)
stat.test

image-20230616112934079

可视化绘图：

p2 = ggplot(pan.meta, aes(x = Group, y = Expression)) + 
  geom_line(aes(group=paired),position = position_dodge(0.2),color="grey80") +
  geom_point(aes(group=paired,size=Expression,
                 # alpha=Expression,
                 fill= Group),pch=21,size=3,
             position = position_jitter(w = 0.1))+
  stat_summary(fun.data = 'mean_se', geom = "errorbar", color = "red", 
               width = 0.25, size = 0.8, position = position_dodge( .9)) +
  facet_wrap(~Type ,scale="free_y",nrow = 2) + 
  scale_fill_manual(values= fill.color) +
  scale_x_discrete( labels=NULL ) + 
  stat_pvalue_manual(stat.test,label = "p.adj", #p.adj.signif
                     tip.length = 0.05,
                     # y.position = 6,
                     remove.bracket = T,hjust=1)+
  labs(x= NULL,y="Gene expression")+
  theme_bw() + mytheme
p2

P值太长了，这里可以用星号，或者是科学计数法显示保留前两位小数：

用星号

###用星号
p3 = ggplot(pan.meta, aes(x = Group, y = Expression)) + 
  geom_line(aes(group=paired),position = position_dodge(0.2),color="grey80") +
  geom_point(aes(group=paired,size=Expression,
                 # alpha=Expression,
                 fill= Group),pch=21,size=3,
             position = position_jitter(w = 0.1))+
  stat_summary(fun.data = 'mean_se', geom = "errorbar", color = "red", 
               width = 0.25, size = 0.8, position = position_dodge( .9)) +
  facet_wrap(~Type ,scale="free_y",nrow = 2) + 
  scale_fill_manual(values= fill.color) +
  scale_x_discrete( labels=NULL ) + 
  stat_pvalue_manual(stat.test,label = "p.adj.signif",
                     tip.length = 0.05,
                     # y.position = 6,
                     remove.bracket = F)+
  labs(x= NULL,y="Gene expression")+
  theme_bw() + mytheme
p3

image-20230616114931024

科学计数法显示保留前两位小数

###科学计数法显示保留前两位小数
stat.test$p.adj <- format(stat.test$p.adj, scientific = TRUE) %>% as.numeric()
stat.test$p.adj <- sprintf("%.2e", stat.test$p.adj)
stat.test$p.adj = ifelse(as.numeric(stat.test$p.adj) > 0.05,"NS",stat.test$p.adj)

p4 = ggplot(pan.meta, aes(x = Group, y = Expression)) + 
  geom_line(aes(group=paired),position = position_dodge(0.2),color="grey80") +
  geom_point(aes(group=paired,size=Expression,
                 # alpha=Expression,
                 fill= Group),pch=21,size=3,
             position = position_jitter(w = 0.1))+
  stat_summary(fun.data = 'mean_se', geom = "errorbar", color = "red", 
               width = 0.25, size = 0.8, position = position_dodge( .9)) +
  facet_wrap(~Type ,scale="free_y",nrow = 2) + 
  scale_fill_manual(values= fill.color) +
  scale_x_discrete( labels=NULL ) + 
  stat_pvalue_manual(stat.test,label = "p.adj", #p.adj.signif
                     tip.length = 0.05,
                     # y.position = 6,
                     remove.bracket = T,hjust=1)+
  labs(x= NULL,y="Gene expression")+
  theme_bw() + mytheme
p4

写在最后

其实我的ggplot2功底也不是很好，没有很系统的去钻研ggplot2的语法和结构。因为我认为我只要会修改别人的ggplot2绘图代码，然后把自己想要绘制的各种元素，能转化为语言去进行网络搜索，这样想绘制的图，基于上都可以根据百度谷歌和工具书去实现。绘图当然很重要，但是科研节奏这么紧张，ggplot2的学习到底应该投入多少时间（当然也看悟性），这点见仁见智。最后给大家分享一本我经常翻阅的ggplot2工具书

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原始发表：2023-08-12，如有侵权请联系 cloudcommunity@tencent.com 删除

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