GGPUBR: How to Add Adjusted P-values to a Multi-Panel GGPlot

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plot of chunk ggpubr-add-adjusted-p-values-to-multi-panel-ggplot


11 Apr

GGPUBR: How to Add Adjusted P-values to a Multi-Panel GGPlot

Alboukadel

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FAQ

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# Load required R packages
library(ggpubr)
library(rstatix)

# Data preparation
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"
  )

# Summary statistics
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
##    <chr>       <chr>       <chr> <chr>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
##  1 cancer      Breast      Gene1 express…     2   9.7  17.6   13.6  3.95  13.6
##  2 normal      Breast      Gene1 express…     3  19.2  34.5   21.9  7.65  25.2
##  3 cancer      Liver       Gene1 express…     5  22.4  30.9   25.5  2.8   26.1
##  4 normal      Liver       Gene1 express…     4  69.9 102.    78.3 10.6   82.0
##  5 cancer      Lung        Gene1 express…     3  19.7  25.8   21.2  3.05  22.2
##  6 normal      Lung        Gene1 express…     3  43.5  51.9   49.5  4.2   48.3
##  7 cancer      Breast      Gene2 express…     3   9.7  15.2   14.5  2.75  13.1
##  8 normal      Breast      Gene2 express…     2  12.6  30     21.3  8.7   21.3
##  9 cancer      Liver       Gene2 express…     2  24.5  26.4   25.4  0.95  25.4
## 10 normal      Liver       Gene2 express…     7  64.5  97.5   79.2 11.1   79.5
## 11 cancer      Lung        Gene2 express…     4  19.7  25.8   23.2  4.53  23.0
## 12 normal      Lung        Gene2 express…     2  49.5  67.5   58.5  9     58.5
## # … with 3 more variables: sd <dbl>, se <dbl>, ci <dbl>
# Statistical test
# group the data by cancer type and gene
# Compare expression values of normal and cancer samples
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
## * <chr>       <chr> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl>
## 1 Breast      Gene1 expr… cancer normal     2     3    -1.88   2.92 1.60e-1 9.60e-1
## 2 Breast      Gene2 expr… cancer normal     3     2    -0.921  1.08 5.17e-1 1.00e+0
## 3 Liver       Gene1 expr… cancer normal     5     4    -7.96   3.26 3.04e-3 1.82e-2
## 4 Liver       Gene2 expr… cancer normal     2     7   -12.6    6.53 7.90e-6 4.74e-5
## 5 Lung        Gene1 expr… cancer normal     3     3    -8.41   3.67 1.58e-3 9.48e-3
## 6 Lung        Gene2 expr… cancer normal     4     2    -3.89   1.06 1.50e-1 9.00e-1
## # … with 1 more variable: p.adj.signif <chr>
# Create boxplot
bxp <- ggboxplot(
  df, x = "sample_type", y = "expression", 
  facet.by = c("gene", "cancer_type")
  ) +
  rotate_x_text(angle = 60)

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

plot of chunk ggpubr-add-adjusted-p-values-to-multi-panel-ggplot

# Display the p-value significance levels
bxp + stat_pvalue_manual(stat.test, label = "p.adj.signif")

plot of chunk ggpubr-add-adjusted-p-values-to-multi-panel-ggplot

# Hide ns and change the bracket tip length
bxp + stat_pvalue_manual(
  stat.test, label = "p.adj.signif",
  hide.ns = TRUE, tip.length = 0
  )

plot of chunk ggpubr-add-adjusted-p-values-to-multi-panel-ggplot

# Show p-values and significance levels
bxp + stat_pvalue_manual(
  stat.test, label = "{p.adj}{p.adj.signif}",
  hide.ns = TRUE, tip.length = 0
  )

plot of chunk ggpubr-add-adjusted-p-values-to-multi-panel-ggplot



 
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Comments ( 2 )

  • Berman
    01 Sep 2020

    Hello Dr. Kassambara,
    Firstly, Thank you for your efforts to teach and show us. If your videos come out, we will be waiting impatiently. Thank for this amazing website too.

    Secondly, I want to ask a qestion about adding p values, as I have read the articles about this package”ggstatplot”, it is very similar to ggplot/ggplot2. Thats why I want to ask my question under of this section.
    I created a demo set to ask my question better. Two questions in this set. I hope you have time and this package is familiar to you.
    Sincerely Thank you in advance for your time and reading.

    library(rstatix)
    library(ggpubr)
    library(ggstatplot)

    # Demo data

    hip <- data.frame(
    stringsAsFactors = FALSE,
    id = c(1L,2L,3L,4L,5L,6L,7L,8L,
    9L,10L,11L,12L,13L,14L,15L,16L,17L,18L,19L,
    20L,21L),
    Group = c("LOW","LOW","LOW","LOW",
    "LOW","LOW","LOW","LOW","LOW","LOW","HIGH","HIGH",
    "HIGH","HIGH","HIGH","HIGH","HIGH","HIGH","HIGH",
    "HIGH","HIGH"),
    Non_Fatigue = c(0.54,0.35,0.69,0.6,0.5,
    0.56,0.72,0.3,0.56,0.63,0.4,0.46,0.35,0.7,0.54,
    0.46,0.35,0.39,0.62,0.52,0.45),
    Fatigue = c(0.6,0.38,0.82,0.5,0.51,
    0.68,0.73,0.38,0.7,0.54,0.62,0.37,0.32,0.85,0.73,
    0.49,0.56,0.29,0.79,0.54,0.48)
    )

    ###########to make TIDY DATA####

    hip %
    gather(key = “Condition”, value = “Velocity”, Fatigue,Non_Fatigue) %>%
    convert_as_factor(id, Condition)

    ######REORDER FOR GRAPHICS###########(dont know any other way,maybe its not logical)

    hip$Condition <- factor(hip$Condition,levels = c("Non_Fatigue", "Fatigue"))

    #####ANOVA#####

    res.aov <- anova_test (data = hip, dv = Velocity, wid = id, between = Group, within = Condition)

    res.aov

    ####Report visiual(WITHIN )

    #!!!!!!!!QUESTION ONE###(((((Is there any way to show p_value between colums (like="*" or "p + labs(subtitle = get_test_label(res.aov, detailed = TRUE, row = 2 ….. It works but other values gone :/

    ##Is there possible way to add this value(res.aov, row=2) ?(((like: F(1,19)=5,97, p=0,024,eta=0,04, d(cohen)=0,39 CI(95%)[0,99-0,32], n(obs)=42 )))

    ggbetweenstats(hip,Condition,Velocity, pairwise.comparisons = TRUE ,pairwise.display = “significant”,

    effsize.type = “biased”,

    type = “p”, p.adjust.method = “none” ,var.equal = T,

    plot.type = “box” ,results.subtitle = T, bf.message = F ,

    mean.ci = F, notch = F,linetype = “solid”,test.value= 2)

    Reply
    • Berman
      01 Sep 2020

      ####Report visiual(WITHIN )

      ####(((((Is there any way to show p_value between colums (like=”*” or “p<0.05") for this graph?)))))

      ggwithinstats(hip,Condition,Velocity,effsize.type = "biased",
      test.value=1,
      type = "p", k=3, centrality.parameter="none" ,centrality.k=4,
      messages = F ,results.subtitle = T, grouping.var = Group)

      #######Report visiual2

      ###Is there any way to show as a subtitle (res.aov, row=2 ) values for this graph?

      ### Actually I run "+ labs(subtitle = get_test_label(res.aov, detailed = TRUE, row = 2))" It works but other values gone..

      ##Is there possible way to add this value?(((like: F(1,19)=5,97, p=0,024,eta=0,04,d(cohen)=0,39 CI(95%)[0,99-0,32], n(obs)=42)))

      ggbetweenstats(hip,Condition,Velocity, pairwise.comparisons = TRUE ,pairwise.display = "significant",

      effsize.type = "biased",

      type = "p", p.adjust.method = "none" ,var.equal = T,

      plot.type = "box" ,results.subtitle = T, bf.message = F ,

      mean.ci = F, notch = F,linetype = "solid",test.value= 2)

      Reply

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