Nature图表复现|方差分析误差线图

R语言数据分析指南

发布于 2023-09-24 14:40:52

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发布于 2023-09-24 14:40:52

❝本节来复现「nature communications」中的一张论文图，「进行单向方差分析并做数据可视化」。由于作者为提供原始数据，因此结果会有所不同。下面小编通过一个案例来进行展示，图形仅供展示用过程仅参考，希望各位观众老爷能够喜欢。数据代码已经整合上传到2023VIP交流群，加群的观众老爷可自行下载，有需要的朋友可关注文末介绍加入VIP交流群 ❞

论文

论文原图

加载R包

library(tidyverse)
library(ggtext)
library(ggprism)
library(ggsignif)
library(rstatix)
library(ggpubr)

导入数据

df <- read_tsv("data.txt") %>% pivot_longer(-Type) %>% 
  dplyr::filter(Type=="Day 5") %>% select(-Type)

方差分析

result.aov <- aov(value ~ name, data = df)
result.tukey <- TukeyHSD(result.aov)

# 转换p值
aov_pvalue <- result.tukey$name %>% as.data.frame() %>% 
  rownames_to_column(var="group") %>% 
  dplyr::select(1,`p adj`) %>% 
  separate(`group`, into=c("group2", "group1"), sep="CAR T-", convert = TRUE) %>% 
  mutate(group1=str_replace_all(`group1`, "CAR T","")) %>% 
  select(2,1,3) %>% 
  select(-1,-2) %>% 
  mutate(p_signif=symnum(`p adj`, corr = FALSE, na = FALSE,  
                         cutpoints = c(0, 0.01, 0.05,1), 
                         symbols = c("**", "*", "ns")))

构建显著性标记文件

❝在此先使用rstatix包内置的wilcox_test函数来进行统计分析得到显著性的位置信息，之后将方差分析的结果合并这样方便后期使用函数自动添加显著性标记。 ❞

df_pvalue <- df %>% 
  mutate(`name`=str_replace_all(`name`, "CAR T","")) %>% 
  mutate(name=str_trim(name)) %>% 
  wilcox_test(value ~ name) %>% 
  add_significance(p.col="p.adj") %>% 
  add_xy_position(x="name") %>% select(-p.adj) %>% 
  bind_cols(aov_pvalue)

数据可视化

df %>%
  mutate(`name`=str_replace(`name`, "CAR T","")) %>% 
  ggplot(aes(name,value))+
  stat_summary(fun = mean,geom = "errorbar", width=.2,
               fun.max = function(x) mean(x) + sd(x),
               fun.min = function(x) mean(x) - sd(x)) +
  stat_summary(fun = mean, geom = "crossbar",width = 0.4,color = "black",size=0.5) +
  geom_jitter(aes(fill=name,color=name,shape=name),width = 0.1, height = 0)+
  stat_pvalue_manual(df_pvalue,label="p_signif",label.size=5,hide.ns=T)+
  scale_shape_manual(values = c(21,22,23,24)) +
  scale_fill_manual(values=c("#679289","#ee2e31","#c9cba3","#f4c095"))+
  scale_color_manual(values=c("#679289","#ee2e31","#c9cba3","#f4c095"))+
  scale_y_continuous(guide = "prism_minor",
                     limits = c(0, 40),
                     expand = c(0, 0))+
  labs(x=NULL,y="CAR T cells x (10<sup>3</sup>/g)")+
  theme_prism()+
  theme(strip.background = element_blank(),
        legend.background = element_rect(color=NA),
        legend.key = element_blank(),
        legend.spacing.x = unit(-0.09,"in"),
        legend.spacing.y = unit(-0.09,"in"),
        legend.text = element_text(color="black",size=6,face="bold"),
        legend.position = "top",
        axis.text.x=element_blank(),
        axis.text.y=element_text(color="black",size=8),
        axis.title.y = element_markdown(color="black",face="bold",size=10),
        axis.ticks.x = element_blank())+
  guides(shape = guide_legend(override.aes = list(size=3),
                              direction = "horizontal",
                              nrow=3, byrow=TRUE))