GGPUBR : Comment Ajouter des P-values Ajustées à un GGPlot Multi-Panel

# Charger les packages R requis
library(ggpubr)
library(rstatix)

# Préparation des données
df <- tibble::tribble(
  ~sample_type, ~expression, ~cancer_type,   ~gene,
      "cancer",        25.8,       "Lung", "Gene1",
      "cancer",        25.5,      "Liver", "Gene1",
      "cancer",        22.4,      "Liver", "Gene1",
      "cancer",        21.2,       "Lung", "Gene1",
      "cancer",        24.5,      "Liver", "Gene1",
      "cancer",        27.3,      "Liver", "Gene1",
      "cancer",        30.9,      "Liver", "Gene1",
      "cancer",        17.6,     "Breast", "Gene1",
      "cancer",        19.7,       "Lung", "Gene1",
      "cancer",         9.7,     "Breast", "Gene1",
      "cancer",        15.2,     "Breast", "Gene2",
      "cancer",        26.4,      "Liver", "Gene2",
      "cancer",        25.8,       "Lung", "Gene2",
      "cancer",         9.7,     "Breast", "Gene2",
      "cancer",        21.2,       "Lung", "Gene2",
      "cancer",        24.5,      "Liver", "Gene2",
      "cancer",        14.5,     "Breast", "Gene2",
      "cancer",        19.7,       "Lung", "Gene2",
      "cancer",        25.2,       "Lung", "Gene2",
      "normal",        43.5,       "Lung", "Gene1",
      "normal",        76.5,      "Liver", "Gene1",
      "normal",        21.9,     "Breast", "Gene1",
      "normal",        69.9,      "Liver", "Gene1",
      "normal",       101.7,      "Liver", "Gene1",
      "normal",        80.1,      "Liver", "Gene1",
      "normal",        19.2,     "Breast", "Gene1",
      "normal",        49.5,       "Lung", "Gene1",
      "normal",        34.5,     "Breast", "Gene1",
      "normal",        51.9,       "Lung", "Gene1",
      "normal",        67.5,       "Lung", "Gene2",
      "normal",          30,     "Breast", "Gene2",
      "normal",        76.5,      "Liver", "Gene2",
      "normal",        88.5,      "Liver", "Gene2",
      "normal",        69.9,      "Liver", "Gene2",
      "normal",        49.5,       "Lung", "Gene2",
      "normal",        80.1,      "Liver", "Gene2",
      "normal",        79.2,      "Liver", "Gene2",
      "normal",        12.6,     "Breast", "Gene2",
      "normal",        97.5,      "Liver", "Gene2",
      "normal",        64.5,      "Liver", "Gene2"
  )

# Statistiques descriptives
df %>%
  group_by(gene, cancer_type, sample_type) %>%
  get_summary_stats(expression, type = "common")

## # A tibble: 12 x 13
##    sample_type cancer_type gene  variable       n   min   max median   iqr  mean    sd    se     ci
##    <chr>       <chr>       <chr> <chr>      <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
##  1 cancer      Breast      Gene1 expression     2   9.7  17.6   13.6  3.95  13.6  5.59  3.95  50.2 
##  2 normal      Breast      Gene1 expression     3  19.2  34.5   21.9  7.65  25.2  8.17  4.72  20.3 
##  3 cancer      Liver       Gene1 expression     5  22.4  30.9   25.5  2.8   26.1  3.20  1.43   3.98
##  4 normal      Liver       Gene1 expression     4  69.9 102.    78.3 10.6   82.0 13.8   6.88  21.9 
##  5 cancer      Lung        Gene1 expression     3  19.7  25.8   21.2  3.05  22.2  3.18  1.84   7.90
##  6 normal      Lung        Gene1 expression     3  43.5  51.9   49.5  4.2   48.3  4.33  2.50  10.7 
##  7 cancer      Breast      Gene2 expression     3   9.7  15.2   14.5  2.75  13.1  2.99  1.73   7.44
##  8 normal      Breast      Gene2 expression     2  12.6  30     21.3  8.7   21.3 12.3   8.7  111.  
##  9 cancer      Liver       Gene2 expression     2  24.5  26.4   25.4  0.95  25.4  1.34  0.95  12.1 
## 10 normal      Liver       Gene2 expression     7  64.5  97.5   79.2 11.1   79.5 11.0   4.17  10.2 
## 11 cancer      Lung        Gene2 expression     4  19.7  25.8   23.2  4.53  23.0  2.99  1.50   4.76
## 12 normal      Lung        Gene2 expression     2  49.5  67.5   58.5  9     58.5 12.7   9    114.

# Test statistique
# regrouper les données par type de cancer et par gène
# Comparer les valeurs d'expression d'échantillons normaux et cancéreux
stat.test <- df %>%
  group_by(cancer_type, gene) %>%
  t_test(expression ~ sample_type) %>%
  adjust_pvalue(method = "bonferroni") %>%
  add_significance()
stat.test

## # A tibble: 6 x 12
##   cancer_type gene  .y.        group1 group2    n1    n2 statistic    df         p     p.adj p.adj.signif
## * <chr>       <chr> <chr>      <chr>  <chr>  <int> <int>     <dbl> <dbl>     <dbl>     <dbl> <chr>       
## 1 Breast      Gene1 expression cancer normal     2     3    -1.88   2.92 0.16      0.96      ns          
## 2 Breast      Gene2 expression cancer normal     3     2    -0.921  1.08 0.517     1         ns          
## 3 Liver       Gene1 expression cancer normal     5     4    -7.96   3.26 0.00304   0.0182    *           
## 4 Liver       Gene2 expression cancer normal     2     7   -12.6    6.53 0.0000079 0.0000474 ****        
## 5 Lung        Gene1 expression cancer normal     3     3    -8.41   3.67 0.00158   0.00948   **          
## 6 Lung        Gene2 expression cancer normal     4     2    -3.89   1.06 0.15      0.900     ns

# Créer un boxplot
bxp <- ggboxplot(
  df, x = "sample_type", y = "expression", 
  facet.by = c("gene", "cancer_type")
  ) +
  rotate_x_text(angle = 60)

# Ajouter les p-values ajustées
stat.test <- stat.test %>% add_xy_position(x = "sample_type")
bxp + stat_pvalue_manual(stat.test, label = "p.adj")

# Afficher les niveaux de significativité de la p-value
bxp + stat_pvalue_manual(stat.test, label = "p.adj.signif")

# Cacher les ns et modifier la longueur de la pointe du crochet
bxp + stat_pvalue_manual(
  stat.test, label = "p.adj.signif",
  hide.ns = TRUE, tip.length = 0
  )

# Afficher les p-values et les niveaux de significativité
bxp + stat_pvalue_manual(
  stat.test, label = "{p.adj}{p.adj.signif}",
  hide.ns = TRUE, tip.length = 0
  )