添加p-value和显著性标记：ggsignif和ggpubr

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2. ggsignif

2.1 ggsignif介绍

ggsignif包主要函数为： geom_signif() 和 stat_signif() ，常用 geom_signif() 。
# geom_signif参数
geom_signif(mapping = NULL, data = NULL, stat = "signif",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, comparisons = NULL, test = "wilcox.test",
test.args = NULL, annotations = NULL, map_signif_level = FALSE,
y_position = NULL, xmin = NULL, xmax = NULL, margin_top = 0.05,
step_increase = 0, tip_length = 0.03, size = 0.5, textsize = 3.88,
family = "", vjust = 0, ...)
library(patchwork) #载入拼图包
head(iris)
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa
# 3          4.7         3.2          1.3         0.2  setosa
# 4          4.6         3.1          1.5         0.2  setosa
# 5          5.0         3.6          1.4         0.2  setosa
# 6          5.4         3.9          1.7         0.4  setosa
Species的三组两两分别作差异性检验，提前设定好配对分析的list
compaired <- list(c("versicolor", "virginica"), 
                  c("versicolor","setosa"), 
                  c("virginica","setosa"))
绘制geom_boxplot()和小提琴图geom_violin()
ggthemr("flat")
p1 <- ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
    geom_boxplot() +
    ylim(1.5, 6.5) +
    geom_signif(comparisons = compaired,
                step_increase = 0.3,
                map_signif_level = F,
                test = wilcox.test)
p2 <- ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
    geom_violin() +
    ylim(1.5, 6.5) +
    geom_signif(comparisons = compaired,
                step_increase = 0.3,
                map_signif_level = T, #修改参数map_signif_level=TRUE
                test = wilcox.test)
p1|p2
compare_means():可以进行一组或多组间的比较。
compare_means(formula, data, method = "wilcox.test", paired = FALSE,
group.by = NULL, ref.group = NULL, ...)
stat_compare_mean():自动添加p-value、显著性标记到ggplot图中
stat_compare_means(mapping = NULL, comparisons = NULL hide.ns = FALSE,
label = NULL, label.x = NULL, label.y = NULL, ...)
compare_means(len~supp, data=ToothGrowth)
## A tibble: 1 x 8
#  .y.   group1 group2      p p.adj p.format p.signif method 
#  <chr> <chr>  <chr>   <dbl> <dbl> <chr>    <chr>    <chr>  
#1 len   OJ     VC     0.0645 0.064 0.064    ns       Wilcox…
y:测试中使用的y变量

p:p-value

p.adj:调整后的p-value。默认为p.adjust.method=“holm”

p.format:四舍五入后的p-value

p.signif:显著性水平

method:用于统计检验的方法
绘制箱线图
p1 <- ggboxplot(ToothGrowth, x="supp", y="len", color = "supp",
               palette = "lancet", add = "jitter")#添加p-valuep+stat_compare_means()
#使用其他统计检验方法
p2 <- p1+stat_compare_means(method = "t.test")
p1|p2
上述显著性标记可以通过label.x、label.y、hjust及vjust来调整

显著性标记可以通过aes()映射来更改：

aes(label=…p.format…)或aes(lebel=paste0(“p=”,…p.format…))：只显示p-value，不显示统计检验方法

aes(label=…p.signif…)：仅显示显著性水平

aes(label=paste0(…method…,"\n", “p=”,…p.format…))：p-value与显著性水平分行显示

也可以将标签指定为字符向量，不要映射，只需将p.signif两端的…去掉即可
3.3 配对样本
compare_means(len~supp, data=ToothGrowth, paired = TRUE)
## A tibble: 1 x 8
#  .y.   group1 group2       p  p.adj p.format p.signif
#  <chr> <chr>  <chr>    <dbl>  <dbl> <chr>    <chr>   
#1 len   OJ     VC     0.00431 0.0043 0.0043   **      
## … with 1 more variable: method <chr>
利用ggpaired()进行可视化
ggpaired(ToothGrowth, x="supp", y="len", color = "supp", line.color = "gray",
line.size = 0.4, palette = "jco")+ stat_compare_means(paired = TRUE)
3.4 多组比较 Global test
compare_means(len~dose, data=ToothGrowth, method = "anova")
## A tibble: 1 x 6
#  .y.          p   p.adj p.format p.signif method
#  <chr>    <dbl>   <dbl> <chr>    <chr>    <chr> 
#1 len   9.53e-16 9.5e-16 9.5e-16  ****     Anova 
p1=ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+
stat_compare_means()
#使用其他的方法
p2=ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+
stat_compare_means(method = "anova")
p1|p2
Pairwise comparisons:如果分组变量中包含两个以上的水平，那么会自动进行pairwise test,默认方法为”wilcox.test”
compare_means(len~dose, data=ToothGrowth)
## A tibble: 3 x 8
#  .y.   group1 group2           p    p.adj p.format p.signif
#  <chr> <chr>  <chr>        <dbl>    <dbl> <chr>    <chr>   
#1 len   0.5    1          7.02e-6   1.4e-5 7.0e-06  ****    
#2 len   0.5    2          8.41e-8   2.5e-7 8.4e-08  ****    
#3 len   1      2          1.77e-4   1.8e-4 0.00018  ***     
## … with 1 more variable: method <chr>
#可以指定比较哪些组
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
p1=ggboxplot(ToothGrowth, x="dose", y="len", color = "dose",palette = "jco")+
  stat_compare_means(comparisons=my_comparisons)+ # Add pairwisecomparisons p-value 
  stat_compare_means(label.y = 50) # Add global p-value
#可以通过修改参数label.y来更改标签的位置
p2=ggboxplot(ToothGrowth, x="dose", y="len", color = "dose",palette = "jco")+
  stat_compare_means(comparisons=my_comparisons, label.y = c(29, 35, 40))+
  # Add pairwise comparisons p-value
  stat_compare_means(label.y = 45) # Add global p-value
p1|p2