如何使用自由比例（不同的 y 高度）自动分配 stat_compare_means() 中的 label.y 位置

Question

我有以下数据框：

library(tidyverse)
library(ggpubr)
    
    dat <- structure(list(genes = c("Gene1", "Gene2", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene1", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", 
"Gene1", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene1", "Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene2", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene2", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene2", "Gene1", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene1", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene2", "Gene1", "Gene1", 
"Gene2", "Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene1", "Gene2", "Gene2", "Gene2", 
"Gene2", "Gene2", "Gene1", "Gene1", "Gene1", "Gene1", "Gene1", 
"Gene2", "Gene1", "Gene1", "Gene2", "Gene1", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene1", "Gene2", "Gene1", "Gene1", "Gene1", 
"Gene1", "Gene1", "Gene1", "Gene2", "Gene2", "Gene2", "Gene2", 
"Gene1", "Gene2", "Gene2", "Gene2", "Gene1", "Gene1", "Gene1"
), gexp = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 
0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 3, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 
0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 
0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0), GROUP = c("Bc_Lin+", "Hd_Lin-", "Hd_Lin+", "Hd_Lin+", "Tl_Lin-", 
"Bc_Lin-", "Hd_Lin-", "Bc_Lin-", "Ab_Lin-", "Bc_Lin+", "Hd_Lin-", 
"Bc_Lin+", "Ab_Lin+", "Tl_Lin+", "Bc_Lin-", "Ab_Lin-", "Hd_Lin-", 
"Bc_Lin-", "Bc_Lin+", "Hd_Lin+", "Bc_Lin+", "Ab_Lin-", "Bc_Lin+", 
"Bc_Lin-", "Bc_Lin+", "Hd_Lin-", "Bc_Lin+", "Bc_Lin+", "Tl_Lin+", 
"Hd_Lin-", "Ab_Lin+", "Hd_Lin+", "Bc_Lin-", "Hd_Lin-", "Ab_Lin+", 
"Ab_Lin+", "Hd_Lin-", "Hd_Lin-", "Bc_Lin+", "Ab_Lin-", "Hd_Lin+", 
"Bc_Lin+", "Ab_Lin+", "Tl_Lin+", "Bc_Lin-", "Hd_Lin+", "Hd_Lin-", 
"Ab_Lin-", "Ab_Lin-", "Ab_Lin-", "Hd_Lin-", "Ab_Lin-", "Bc_Lin-", 
"Hd_Lin+", "Tl_Lin+", "Bc_Lin+", "Bc_Lin-", "Ab_Lin-", "Tl_Lin+", 
"Ab_Lin-", "Ab_Lin+", "Hd_Lin+", "Hd_Lin+", "Ab_Lin-", "Tl_Lin+", 
"Bc_Lin+", "Hd_Lin+", "Ab_Lin-", "Bc_Lin-", "Bc_Lin-", "Ab_Lin+", 
"Bc_Lin+", "Ab_Lin-", "Bc_Lin-", "Bc_Lin+", "Hd_Lin+", "Bc_Lin+", 
"Hd_Lin+", "Hd_Lin+", "Bc_Lin-", "Hd_Lin-", "Bc_Lin-", "Tl_Lin+", 
"Hd_Lin+", "Bc_Lin-", "Ab_Lin-", "Bc_Lin-", "Tl_Lin+", "Bc_Lin+", 
"Tl_Lin+", "Hd_Lin+", "Tl_Lin+", "Hd_Lin-", "Hd_Lin-", "Hd_Lin+", 
"Tl_Lin+", "Bc_Lin-", "Hd_Lin-", "Ab_Lin+", "Hd_Lin-", "Hd_Lin+", 
"Bc_Lin+", "Tl_Lin+", "Tl_Lin+", "Ab_Lin-", "Ab_Lin+", "Tl_Lin+", 
"Tl_Lin+", "Ab_Lin-", "Ab_Lin-", "Ab_Lin-", "Bc_Lin+", "Hd_Lin+", 
"Ab_Lin-", "Ab_Lin+", "Hd_Lin-", "Bc_Lin-", "Ab_Lin+", "Hd_Lin+", 
"Bc_Lin-", "Tl_Lin+", "Hd_Lin-", "Hd_Lin+", "Ab_Lin-", "Ab_Lin-", 
"Hd_Lin-", "Hd_Lin-", "Bc_Lin-", "Ab_Lin+", "Hd_Lin-", "Hd_Lin-", 
"Ab_Lin-", "Hd_Lin+", "Hd_Lin-", "Ab_Lin-", "Ab_Lin-", "Hd_Lin-", 
"Bc_Lin-", "Hd_Lin-", "Ab_Lin+", "Bc_Lin-", "Tl_Lin-", "Bc_Lin+", 
"Bc_Lin+", "Ab_Lin+", "Hd_Lin-", "Ab_Lin-", "Tl_Lin-", "Bc_Lin-", 
"Bc_Lin-", "Ab_Lin-", "Hd_Lin+", "Hd_Lin+", "Bc_Lin-", "Hd_Lin-", 
"Bc_Lin-", "Bc_Lin+", "Bc_Lin+", "Bc_Lin+", "Ab_Lin+", "Bc_Lin-", 
"Ab_Lin-", "Ab_Lin-", "Tl_Lin+", "Ab_Lin+", "Ab_Lin-", "Ab_Lin+", 
"Hd_Lin+", "Ab_Lin+", "Bc_Lin+", "Tl_Lin+", "Bc_Lin+", "Ab_Lin+", 
"Hd_Lin+", "Ab_Lin-", "Tl_Lin+", "Hd_Lin-", "Bc_Lin-", "Hd_Lin-", 
"Hd_Lin-", "Bc_Lin+", "Ab_Lin+", "Ab_Lin+", "Tl_Lin+", "Ab_Lin-", 
"Hd_Lin-", "Ab_Lin+", "Ab_Lin+", "Bc_Lin+", "Bc_Lin+", "Ab_Lin+", 
"Tl_Lin+", "Tl_Lin+", "Ab_Lin+", "Hd_Lin+", "Bc_Lin-", "Hd_Lin+", 
"Ab_Lin+", "Hd_Lin-", "Ab_Lin-", "Hd_Lin-", "Ab_Lin+", "Tl_Lin+", 
"Hd_Lin-", "Bc_Lin-", "Tl_Lin+", "Bc_Lin-", "Ab_Lin-", "Hd_Lin+", 
"Tl_Lin+", "Hd_Lin+", "Ab_Lin-", "Ab_Lin+", "Ab_Lin-", "Bc_Lin-", 
"Hd_Lin+", "Hd_Lin-", "Hd_Lin+", "Ab_Lin-", "Ab_Lin-", "Ab_Lin-", 
"Hd_Lin+", "Bc_Lin+", "Hd_Lin+", "Hd_Lin+", "Ab_Lin-", "Hd_Lin+", 
"Tl_Lin+", "Hd_Lin+", "Hd_Lin+", "Hd_Lin-", "Tl_Lin+", "Hd_Lin+", 
"Hd_Lin-", "Ab_Lin-", "Bc_Lin+", "Bc_Lin+", "Tl_Lin+", "Ab_Lin+", 
"Bc_Lin+", "Hd_Lin-", "Bc_Lin-", "Ab_Lin+", "Tl_Lin+", "Bc_Lin+", 
"Hd_Lin+", "Tl_Lin-", "Tl_Lin+", "Ab_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Ab_Lin-", "Ab_Lin-", "Ab_Lin+", "Hd_Lin+", "Hd_Lin-", "Ab_Lin-", 
"Tl_Lin+", "Bc_Lin-", "Bc_Lin-", "Bc_Lin-", "Bc_Lin+", "Bc_Lin+", 
"Ab_Lin-", "Tl_Lin+", "Ab_Lin-", "Bc_Lin-", "Ab_Lin-", "Ab_Lin-", 
"Ab_Lin+", "Bc_Lin-", "Ab_Lin+", "Ab_Lin-", "Ab_Lin+", "Tl_Lin-", 
"Bc_Lin+", "Bc_Lin+", "Ab_Lin+", "Tl_Lin-", "Tl_Lin+", "Ab_Lin-", 
"Bc_Lin+", "Bc_Lin-", "Bc_Lin+", "Ab_Lin-", "Ab_Lin+", "Tl_Lin+", 
"Tl_Lin+", "Ab_Lin+", "Bc_Lin-", "Ab_Lin-", "Bc_Lin-", "Tl_Lin+", 
"Tl_Lin-", "Tl_Lin+", "Hd_Lin-", "Hd_Lin+", "Ab_Lin+", "Hd_Lin-", 
"Bc_Lin-", "Bc_Lin-", "Bc_Lin+", "Bc_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Tl_Lin-", "Bc_Lin-", "Hd_Lin+", "Bc_Lin+", "Ab_Lin-", "Ab_Lin-", 
"Ab_Lin-", "Ab_Lin+", "Hd_Lin+", "Ab_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Bc_Lin-", "Hd_Lin+", "Bc_Lin+", "Tl_Lin+", "Hd_Lin+", "Bc_Lin+", 
"Bc_Lin-", "Ab_Lin-", "Tl_Lin+", "Hd_Lin-", "Bc_Lin-", "Bc_Lin-", 
"Hd_Lin-", "Bc_Lin-", "Tl_Lin+", "Ab_Lin-", "Hd_Lin-", "Hd_Lin+", 
"Ab_Lin+", "Hd_Lin+", "Tl_Lin-", "Bc_Lin-", "Bc_Lin-", "Ab_Lin+", 
"Bc_Lin+", "Tl_Lin-", "Tl_Lin+", "Bc_Lin-", "Bc_Lin+", "Hd_Lin+", 
"Bc_Lin+", "Ab_Lin+", "Bc_Lin+", "Bc_Lin-", "Tl_Lin+", "Bc_Lin-", 
"Ab_Lin-", "Bc_Lin-", "Bc_Lin+", "Ab_Lin-", "Hd_Lin+", "Bc_Lin+", 
"Hd_Lin-", "Tl_Lin+", "Tl_Lin+", "Ab_Lin+", "Ab_Lin-", "Ab_Lin+", 
"Bc_Lin+", "Hd_Lin-", "Ab_Lin-", "Tl_Lin-", "Hd_Lin+", "Tl_Lin+", 
"Bc_Lin+", "Hd_Lin+", "Hd_Lin-", "Ab_Lin-", "Bc_Lin+", "Bc_Lin-", 
"Ab_Lin-", "Hd_Lin-", "Hd_Lin-", "Hd_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Bc_Lin+", "Bc_Lin+", "Hd_Lin+", "Bc_Lin+", "Ab_Lin+", "Bc_Lin+", 
"Hd_Lin+", "Bc_Lin+", "Tl_Lin+", "Bc_Lin-", "Tl_Lin+", "Hd_Lin-", 
"Tl_Lin+", "Bc_Lin+", "Bc_Lin+", "Bc_Lin-", "Tl_Lin-", "Bc_Lin-", 
"Tl_Lin+", "Bc_Lin+", "Hd_Lin-", "Ab_Lin+", "Ab_Lin-", "Ab_Lin-", 
"Ab_Lin-", "Ab_Lin-", "Tl_Lin-", "Bc_Lin-", "Ab_Lin+", "Ab_Lin-", 
"Tl_Lin+", "Ab_Lin+", "Ab_Lin-", "Hd_Lin+", "Ab_Lin+", "Bc_Lin-", 
"Bc_Lin+", "Bc_Lin-", "Ab_Lin-", "Tl_Lin+", "Hd_Lin-", "Ab_Lin-", 
"Ab_Lin-", "Ab_Lin+", "Bc_Lin+", "Hd_Lin+", "Tl_Lin+", "Bc_Lin-", 
"Ab_Lin-", "Bc_Lin+", "Tl_Lin+", "Bc_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Ab_Lin-", "Ab_Lin-", "Bc_Lin+", "Bc_Lin-", "Ab_Lin-", "Hd_Lin+", 
"Tl_Lin-", "Hd_Lin+", "Hd_Lin-", "Bc_Lin+", "Ab_Lin+", "Hd_Lin-", 
"Tl_Lin+", "Ab_Lin-", "Bc_Lin+", "Ab_Lin-", "Tl_Lin+", "Bc_Lin-", 
"Bc_Lin-", "Ab_Lin-", "Tl_Lin-", "Bc_Lin+", "Hd_Lin+", "Bc_Lin+", 
"Bc_Lin-", "Hd_Lin+", "Bc_Lin+", "Tl_Lin+", "Ab_Lin+", "Ab_Lin+", 
"Hd_Lin+", "Ab_Lin-", "Bc_Lin+", "Hd_Lin-", "Hd_Lin+", "Ab_Lin-", 
"Hd_Lin-", "Tl_Lin+", "Tl_Lin+", "Hd_Lin-", "Hd_Lin+", "Bc_Lin+", 
"Hd_Lin-", "Bc_Lin-", "Bc_Lin+", "Bc_Lin-", "Bc_Lin-", "Bc_Lin-", 
"Tl_Lin+", "Bc_Lin+", "Ab_Lin-", "Ab_Lin+", "Bc_Lin+", "Bc_Lin+", 
"Tl_Lin+", "Ab_Lin+", "Ab_Lin+", "Hd_Lin-", "Bc_Lin+", "Hd_Lin+", 
"Bc_Lin-", "Ab_Lin+", "Hd_Lin-", "Hd_Lin-", "Tl_Lin+", "Ab_Lin+", 
"Bc_Lin+", "Tl_Lin-", "Bc_Lin-", "Bc_Lin-", "Hd_Lin+", "Ab_Lin-", 
"Bc_Lin+", "Ab_Lin-", "Tl_Lin+", "Ab_Lin+", "Bc_Lin-", "Hd_Lin+", 
"Ab_Lin+", "Tl_Lin+", "Hd_Lin-", "Ab_Lin-", "Bc_Lin+", "Bc_Lin+", 
"Hd_Lin+", "Bc_Lin+", "Tl_Lin+", "Ab_Lin+", "Tl_Lin+", "Ab_Lin+", 
"Bc_Lin-", "Hd_Lin-", "Hd_Lin+", "Hd_Lin-", "Bc_Lin-", "Ab_Lin+", 
"Ab_Lin-", "Hd_Lin+", "Tl_Lin+", "Ab_Lin+", "Ab_Lin+", "Bc_Lin+", 
"Bc_Lin-", "Ab_Lin+", "Ab_Lin-", "Tl_Lin+", "Bc_Lin-", "Bc_Lin-", 
"Tl_Lin+", "Ab_Lin-", "Bc_Lin+", "Hd_Lin+", "Bc_Lin+", "Bc_Lin-", 
"Bc_Lin-", "Tl_Lin+", "Bc_Lin+", "Hd_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Bc_Lin-", "Bc_Lin-", "Hd_Lin-", "Ab_Lin-", "Ab_Lin+", "Bc_Lin-", 
"Bc_Lin+", "Tl_Lin+", "Ab_Lin-", "Ab_Lin+", "Ab_Lin-", "Ab_Lin+", 
"Hd_Lin+", "Tl_Lin+", "Hd_Lin-", "Tl_Lin+", "Ab_Lin-", "Hd_Lin+", 
"Bc_Lin-", "Ab_Lin+", "Hd_Lin+", "Ab_Lin+", "Ab_Lin+", "Ab_Lin+", 
"Ab_Lin-", "Ab_Lin+", "Bc_Lin-", "Hd_Lin+", "Bc_Lin-", "Hd_Lin+", 
"Ab_Lin+", "Bc_Lin+", "Tl_Lin-", "Tl_Lin-", "Hd_Lin-", "Bc_Lin-", 
"Ab_Lin-", "Hd_Lin+", "Hd_Lin+", "Tl_Lin+", "Hd_Lin-", "Ab_Lin-", 
"Bc_Lin-", "Tl_Lin+", "Bc_Lin+", "Hd_Lin-", "Ab_Lin+", "Bc_Lin-", 
"Tl_Lin-", "Hd_Lin+", "Tl_Lin+", "Bc_Lin+", "Ab_Lin+", "Tl_Lin+", 
"Ab_Lin+", "Bc_Lin-", "Bc_Lin-", "Hd_Lin-", "Ab_Lin+", "Bc_Lin-", 
"Bc_Lin-", "Hd_Lin+", "Hd_Lin+", "Ab_Lin-", "Tl_Lin-", "Tl_Lin+", 
"Ab_Lin+", "Tl_Lin+", "Hd_Lin+", "Hd_Lin+", "Hd_Lin-", "Hd_Lin+", 
"Hd_Lin-", "Bc_Lin+", "Ab_Lin+", "Bc_Lin+", "Ab_Lin+", "Tl_Lin+", 
"Bc_Lin-", "Hd_Lin+", "Ab_Lin+", "Bc_Lin-", "Bc_Lin-", "Hd_Lin+", 
"Ab_Lin-", "Hd_Lin+", "Tl_Lin-", "Ab_Lin+", "Bc_Lin+", "Bc_Lin-", 
"Ab_Lin+", "Ab_Lin+", "Hd_Lin+", "Hd_Lin+", "Bc_Lin+", "Bc_Lin-", 
"Ab_Lin+", "Hd_Lin-", "Tl_Lin+", "Bc_Lin+", "Tl_Lin-", "Tl_Lin-", 
"Hd_Lin-", "Bc_Lin-", "Bc_Lin+", "Tl_Lin-", "Hd_Lin+", "Hd_Lin-", 
"Tl_Lin+", "Hd_Lin-", "Bc_Lin+", "Hd_Lin+", "Hd_Lin+", "Bc_Lin+", 
"Ab_Lin-", "Ab_Lin+", "Ab_Lin-", "Hd_Lin-", "Ab_Lin-", "Tl_Lin+", 
"Bc_Lin+", "Bc_Lin-", "Hd_Lin+", "Bc_Lin-", "Ab_Lin+", "Hd_Lin+", 
"Bc_Lin-", "Ab_Lin+", "Bc_Lin-", "Tl_Lin-", "Ab_Lin+", "Hd_Lin-", 
"Bc_Lin+", "Ab_Lin-", "Hd_Lin+", "Hd_Lin+", "Bc_Lin+", "Ab_Lin+", 
"Bc_Lin-", "Ab_Lin-", "Tl_Lin+", "Ab_Lin-", "Bc_Lin-", "Tl_Lin+", 
"Ab_Lin-", "Hd_Lin-", "Ab_Lin+", "Tl_Lin-", "Tl_Lin+", "Bc_Lin+", 
"Bc_Lin-", "Hd_Lin+", "Bc_Lin-", "Ab_Lin-", "Bc_Lin-", "Ab_Lin-", 
"Hd_Lin+", "Ab_Lin-", "Hd_Lin-", "Ab_Lin-", "Bc_Lin+", "Ab_Lin+", 
"Bc_Lin-", "Bc_Lin-", "Ab_Lin-", "Hd_Lin+", "Ab_Lin+", "Hd_Lin-", 
"Ab_Lin+", "Ab_Lin+", "Hd_Lin+", "Bc_Lin-", "Bc_Lin-", "Bc_Lin-", 
"Ab_Lin-")), row.names = c(NA, -732L), class = c("tbl_df", "tbl", 
"data.frame"))

我要的是对GROUP列进行统计比较。这是我的代码

sort_order <-  c("Hd_Lin+", "Hd_Lin-", 
                 "Ab_Lin+", "Ab_Lin-", 
                 "Bc_Lin+", "Bc_Lin-", 
                 "Tl_Lin+", "Tl_Lin-")

dat <- dat %>% 
       mutate(GROUP = factor(GROUP, levels = sort_order))
       my_comparison <- list(
      c("Hd_Lin+", "Hd_Lin-"), 
      c("Ab_Lin+", "Ab_Lin-"), 
      c("Bc_Lin+", "Bc_Lin-"), 
      c("Tl_Lin+", "Tl_Lin-"))
    
    
    ggpubr::ggbarplot(dat, x = "GROUP", y = "gexp", add = "mean_se", facet.by = "genes", 
                      scales = "free") + 
      stat_compare_means(aes(label = ..p.signif..),
             
                         label.y = c(seq( 0.456, by = 0, length.out = 4),seq( 0.125, by = 0, length.out = 4)),
                         tip.length = 0, vjust = 0.5,
                         method = "t.test", comparisons = my_comparison) + 
      theme(axis.text.x=element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12))  + 
      xlab("")

正如您在下面的图中注意到的那样，Gene1 和 Gene2 的统计测试线相同。

如何针对自由 y 尺度的不同方面自动调整它？

这条线似乎不起作用：

 label.y = c(seq( 0.456, by = 0, length.out = 4),seq( 0.125, by = 0, length.out = 4))

我们可以预先计算出每个基因的最大均值在这个范围内：0.4561404 0.1250000.

Answer 1

这可能是完成任务的一种方式：

结果图：

代码

# your data

sort_order <-  c("Hd_Lin+", "Hd_Lin-", 
                 "Ab_Lin+", "Ab_Lin-", 
                 "Bc_Lin+", "Bc_Lin-", 
                 "Tl_Lin+", "Tl_Lin-")

dat <- dat %>% 
  mutate(GROUP = factor(GROUP, levels = sort_order))
my_comparison <- list(
  c("Hd_Lin+", "Hd_Lin-"), 
  c("Ab_Lin+", "Ab_Lin-"), 
  c("Bc_Lin+", "Bc_Lin-"), 
  c("Tl_Lin+", "Tl_Lin-"))


library(ggpubr)
library(tidyverse)

# data for Gene1
dat1 <- dat %>% 
  filter(genes=="Gene1")

# date for Gene2
dat2 <- dat %>% 
  filter(genes=="Gene2")

# plot p1
p1 <- ggpubr::ggbarplot(dat1, x = "GROUP", y = "gexp", add = "mean_se", facet.by = "genes", 
                  scales = "free") + 
  stat_compare_means(aes(label = ..p.signif..),
                     label.y = c(seq( 0.3, by = 0, length.out = 4)),
                     tip.length = 0, vjust = 0.5,
                     method = "t.test", comparisons = my_comparison) + 
  theme(axis.text.x=element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12))  + 
  xlab("") +
  coord_cartesian(ylim=c(0, 0.6)) +
  scale_y_continuous(breaks = seq(0,0.5,0.1), labels = seq(0.1,0.6,0.1))
  

# plot p2
p2 <- ggpubr::ggbarplot(dat2, x = "GROUP", y = "gexp", add = "mean_se", facet.by = "genes", 
                        scales = "free") + 
  stat_compare_means(aes(label = ..p.signif..),
                     label.y = c(seq( 0.6, by = 0, length.out = 4)),
                     tip.length = 0, vjust = 0.5,
                     method = "t.test", comparisons = my_comparison) + 
  theme(axis.text.x=element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12))  + 
  xlab("") +
  coord_cartesian(ylim=c(0, 0.6)) +
  scale_y_continuous(breaks = seq(0,0.5,0.1), labels = seq(0.1,0.6,0.1))

ggarrange(p1, p2, heights = c(4, 4), nrow = 1, align = "h")

说明

我试图在 facets 中访问 label.y，但失败了。看这里 https://github.com/kassambara/ggpubr/issues/50.

然后我决定应用一个解决方法：想法如下：

为每个基因创建一个单独的图，并通过过滤dat1 将其与自己分面对于 gene1 和 dat2 对于 gene2.
这里的挑战是适应ylim，最后我通过这段代码实现了这个

coord_cartesian(ylim=c(0, 0.6)) +
  scale_y_continuous(breaks = seq(0,0.5,0.1), labels = seq(0.1,0.6,0.1))

现在您可以通过将 ..c(seq(0.3 更改为 ..c(seq(0.4 等来单独更改 stat_compare_means 的位置。
最后，您可以将两个图与 ggpubr 包
中的 ggarrange 结合起来

Answer 2

试试这个。它适用于任何数量的基因。无硬编码：

library(rstatix)
sort_order <- c(
  "Hd_Lin+", "Hd_Lin-",
  "Ab_Lin+", "Ab_Lin-",
  "Bc_Lin+", "Bc_Lin-",
  "Tl_Lin+", "Tl_Lin-"
)


my_comparisons <- list(
  c("Hd_Lin+", "Hd_Lin-"),
  c("Ab_Lin+", "Ab_Lin-"),
  c("Bc_Lin+", "Bc_Lin-"),
  c("Tl_Lin+", "Tl_Lin-")
)

dat <- dat %>% 
  mutate(GROUP = factor(GROUP, levels = sort_order))

stat.test <- dat %>% 
  group_by(genes) %>% 
  t_test(gexp ~ GROUP, p.adjust.method = "none") %>% 
  add_xy_position(x = "GROUP", fun = "mean_se", 
                scales = "free", comparisons = my_comparisons, step.increase = 0) %>% 
  mutate(p.adj.signif = if_else(p.adj.signif == "", "NS.", p.adj.signif)) 
    

p <- ggpubr::ggbarplot(dat, x = "GROUP", y = "gexp", add = "mean_se",
                  facet =  "genes", scales = "free"
                 ) +
  stat_pvalue_manual(stat.test, hide.ns = FALSE, tip.length = 0, bracket.nudge.y = 0.01) +
  # scale_y_continuous(expand = expansion(mult = c(0.05, 0.5))) +
  theme(strip.text.x = element_text(size = 20, colour = "black", face = "bold")) +
  theme(legend.position = "none") +
  xlab("") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p

它产生：

如何使用自由比例（不同的 y 高度）自动分配 stat_compare_means() 中的 label.y 位置

How to automatically assign label.y position in stat_compare_means() under facet with free scale (different y-height)

r

ggplot2

tidyverse

ggpubr